Networks project
Anna Goncharova, Tanish Ramisetty, Samuel Broom
2023-04-27
library(ggplot2)
library(btergm)## Registered S3 methods overwritten by 'btergm':
## method from
## plot.gof ergm
## print.gof ergm## Package: btergm
## Version: 1.10.6
## Date: 2022-04-01
## Authors: Philip Leifeld (University of Essex)
## Skyler J. Cranmer (The Ohio State University)
## Bruce A. Desmarais (Pennsylvania State University)library(ggpubr)
library(ergm)## Loading required package: network##
## 'network' 1.18.1 (2023-01-24), part of the Statnet Project
## * 'news(package="network")' for changes since last version
## * 'citation("network")' for citation information
## * 'https://statnet.org' for help, support, and other information##
## 'ergm' 4.4.0 (2023-01-26), part of the Statnet Project
## * 'news(package="ergm")' for changes since last version
## * 'citation("ergm")' for citation information
## * 'https://statnet.org' for help, support, and other information## 'ergm' 4 is a major update that introduces some backwards-incompatible
## changes. Please type 'news(package="ergm")' for a list of major
## changes.##
## Attaching package: 'ergm'## The following object is masked from 'package:btergm':
##
## goflibrary(readr)
library(magrittr)
library(tidyr) ##
## Attaching package: 'tidyr'## The following object is masked from 'package:magrittr':
##
## extractlibrary(ggnetwork)
library(gplots)##
## Attaching package: 'gplots'## The following object is masked from 'package:stats':
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## lowesslibrary(ggraph)
library(statnet)## Loading required package: tergm## Loading required package: networkDynamic##
## 'networkDynamic' 0.11.3 (2023-02-15), part of the Statnet Project
## * 'news(package="networkDynamic")' for changes since last version
## * 'citation("networkDynamic")' for citation information
## * 'https://statnet.org' for help, support, and other information## Registered S3 method overwritten by 'tergm':
## method from
## simulate_formula.network ergm##
## 'tergm' 4.1.1 (2022-11-07), part of the Statnet Project
## * 'news(package="tergm")' for changes since last version
## * 'citation("tergm")' for citation information
## * 'https://statnet.org' for help, support, and other information##
## Attaching package: 'tergm'## The following object is masked from 'package:ergm':
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## snctrl## Loading required package: ergm.count##
## 'ergm.count' 4.1.1 (2022-05-24), part of the Statnet Project
## * 'news(package="ergm.count")' for changes since last version
## * 'citation("ergm.count")' for citation information
## * 'https://statnet.org' for help, support, and other information## Loading required package: sna## Loading required package: statnet.common##
## Attaching package: 'statnet.common'## The following object is masked from 'package:ergm':
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## snctrl## The following objects are masked from 'package:base':
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## attr, order## sna: Tools for Social Network Analysis
## Version 2.7-1 created on 2023-01-24.
## copyright (c) 2005, Carter T. Butts, University of California-Irvine
## For citation information, type citation("sna").
## Type help(package="sna") to get started.## Loading required package: tsna##
## 'statnet' 2019.6 (2019-06-13), part of the Statnet Project
## * 'news(package="statnet")' for changes since last version
## * 'citation("statnet")' for citation information
## * 'https://statnet.org' for help, support, and other information## unable to reach CRANlibrary(igraph)##
## Attaching package: 'igraph'## The following objects are masked from 'package:sna':
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## betweenness, bonpow, closeness, components, degree, dyad.census,
## evcent, hierarchy, is.connected, neighborhood, triad.census## The following object is masked from 'package:tidyr':
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## crossing## The following objects are masked from 'package:network':
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## %c%, %s%, add.edges, add.vertices, delete.edges, delete.vertices,
## get.edge.attribute, get.edges, get.vertex.attribute, is.bipartite,
## is.directed, list.edge.attributes, list.vertex.attributes,
## set.edge.attribute, set.vertex.attribute## The following objects are masked from 'package:stats':
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## decompose, spectrum## The following object is masked from 'package:base':
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## unionlibrary(igraphdata)
library(sna)data("macaque")
net <- macaque
adj_matrix <- as.matrix(as_adjacency_matrix(macaque))
net_sna <- network(adj_matrix, directed = TRUE)
summary(macaque)## IGRAPH f7130f3 DN-- 45 463 --
## + attr: Citation (g/c), Author (g/c), shape (v/c), name (v/c)is_simple(macaque)## [1] TRUEis_directed(macaque)## [1] TRUEsim_und_net <- igraph::undirected_graph(macaque)
sim_und_net <- as.undirected(macaque)#Creating the first visualization to better understand our network
ggraph(net) +
geom_edge_link(alpha = .25) +
geom_node_point() +
geom_node_label(aes(label = name), repel = T, label.size = 0.1, alpha = 0.8,
color = "grey50", fill = "#D7DFD7") +
ggnetwork::theme_blank()## Using "stress" as default layout## Warning: Using the `size` aesthetic in this geom was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` in the `default_aes` field and elsewhere instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
#Understanding degree (the number of ties a node has)
deg <- igraph::degree(net)
deg## V1 V2 V3 V3A V4 V4t VOT VP MT MSTd/p MSTl
## 16 28 28 25 40 17 10 27 32 33 19
## PO LIP PIP VIP DP 7a FST PITd PITv CITd CITv
## 28 38 16 40 20 24 35 13 20 9 16
## AITd AITv STPp STPa TF TH FEF 46 3a 3b 1
## 14 12 20 9 29 21 38 36 12 8 15
## 2 5 Ri SII 7b 4 6 SMA Ig Id 35
## 20 20 8 23 22 17 20 16 11 7 6
## 36
## 8#Second visualization
ggraph(net) +
geom_edge_link0(color = "black", alpha = .5) +
geom_node_point(fill = "#EFD9D3", color = "black", shape = 1,
size = V(net)$size <- igraph::degree(net, mode = "all")) +
ggnetwork::theme_blank()## Using "stress" as default layout
#Taking a look at in degree (the in-degree of a node is the number of incoming edges that are directed towards the node. In other words, it is a measure of the number of other nodes that have edges pointing to the given node)
deg_in <- igraph::degree(net, mode = "in")
deg_in## V1 V2 V3 V3A V4 V4t VOT VP MT MSTd/p MSTl
## 8 13 14 12 20 8 5 13 16 16 11
## PO LIP PIP VIP DP 7a FST PITd PITv CITd CITv
## 15 18 8 20 10 14 18 5 11 3 8
## AITd AITv STPp STPa TF TH FEF 46 3a 3b 1
## 9 7 10 4 12 9 18 16 6 4 7
## 2 5 Ri SII 7b 4 6 SMA Ig Id 35
## 10 10 4 13 12 9 10 8 6 3 4
## 36
## 6#Directed visualization
ggraph(net) +
geom_edge_link(color = "black", alpha = .5,
arrow = grid::arrow(angle = 20,
length = unit(0.10, "in"),
type = "closed"),
start_cap = circle(2, 'mm'),
end_cap = circle(2, 'mm')) +
geom_node_point(fill = "#EFD9D3", color = "black", shape = 21,
size = igraph::degree(net, mode = "in")) +
ggnetwork::theme_blank()## Using "stress" as default layout
#Taking a look at out degree (In network analysis, the out-degree of a node is the number of outgoing edges that are directed away from the node. In other words, it is a measure of the number of other nodes to which the given node is connected by an edge)
deg_out <- igraph::degree(net, mode = "out")
deg_out## V1 V2 V3 V3A V4 V4t VOT VP MT MSTd/p MSTl
## 8 15 14 13 20 9 5 14 16 17 8
## PO LIP PIP VIP DP 7a FST PITd PITv CITd CITv
## 13 20 8 20 10 10 17 8 9 6 8
## AITd AITv STPp STPa TF TH FEF 46 3a 3b 1
## 5 5 10 5 17 12 20 20 6 4 8
## 2 5 Ri SII 7b 4 6 SMA Ig Id 35
## 10 10 4 10 10 8 10 8 5 4 2
## 36
## 2ggraph(net) +
geom_edge_link(color = "black", alpha = .5,
arrow = grid::arrow(angle = 20,
length = unit(0.10, "in"),
type = "closed"),
start_cap = circle(2, 'mm'),
end_cap = circle(2, 'mm')) +
geom_node_point(fill = "#EFD9D3", color = "black", shape = 21,
size = V(net)$size <- igraph::degree(net, mode = "out")) +
ggnetwork::theme_blank()## Using "stress" as default layout
#Examining Closeness (closeness is a measure of how easily a node can reach other nodes in the network. Specifically, it is the inverse of the sum of the shortest path distances from a node to all other nodes in the network)
close <- igraph::closeness(net)
close ## V1 V2 V3 V3A V4 V4t
## 0.009803922 0.012658228 0.012820513 0.011627907 0.013333333 0.010638298
## VOT VP MT MSTd/p MSTl PO
## 0.009523810 0.012820513 0.013157895 0.013333333 0.011627907 0.011764706
## LIP PIP VIP DP 7a FST
## 0.014285714 0.010101010 0.014084507 0.011904762 0.012195122 0.013333333
## PITd PITv CITd CITv AITd AITv
## 0.011235955 0.011627907 0.009803922 0.010416667 0.010638298 0.010000000
## STPp STPa TF TH FEF 46
## 0.011363636 0.010101010 0.013698630 0.012048193 0.014084507 0.014705882
## 3a 3b 1 2 5 Ri
## 0.009523810 0.007352941 0.009708738 0.009900990 0.010752688 0.008474576
## SII 7b 4 6 SMA Ig
## 0.010101010 0.011494253 0.009708738 0.010989011 0.010752688 0.008403361
## Id 35 36
## 0.007299270 0.006250000 0.006250000ggraph(net) +
geom_edge_link0(color = "black", alpha = .5,
arrow = grid::arrow(angle = 20,
length = unit(0.10, "in"),
type = "closed"),
start_cap = circle(2, 'mm'),
end_cap = circle(2, 'mm')) +
geom_node_point(fill = "#EFD9D3", color = "black", shape = 21,
size = igraph::closeness(net)*15) +
ggnetwork::theme_blank()## Using "stress" as default layout## Warning in geom_edge_link0(color = "black", alpha = 0.5, arrow =
## grid::arrow(angle = 20, : Ignoring unknown parameters: `start_cap` and `end_cap`
ggraph(net) +
geom_edge_link0(color = "black", alpha = .5) +
geom_node_point(fill = "#EFD9D3", color = "black", shape = 21,
size = igraph::closeness(net)*900) +
ggnetwork::theme_blank()## Using "stress" as default layout
#Examining in closeness
close_in <- igraph::closeness(net, mode = "in")
close_in ## V1 V2 V3 V3A V4 V4t
## 0.009433962 0.010204082 0.012195122 0.010416667 0.012048193 0.009708738
## VOT VP MT MSTd/p MSTl PO
## 0.009174312 0.012048193 0.012500000 0.012500000 0.011627907 0.011764706
## LIP PIP VIP DP 7a FST
## 0.012987013 0.009433962 0.013698630 0.010752688 0.012658228 0.012820513
## PITd PITv CITd CITv AITd AITv
## 0.009803922 0.010869565 0.008474576 0.008928571 0.010309278 0.008695652
## STPp STPa TF TH FEF 46
## 0.010204082 0.009174312 0.010989011 0.010416667 0.012820513 0.013157895
## 3a 3b 1 2 5 Ri
## 0.009615385 0.008333333 0.010000000 0.010869565 0.011235955 0.008695652
## SII 7b 4 6 SMA Ig
## 0.012048193 0.012048193 0.011363636 0.011363636 0.010638298 0.009174312
## Id 35 36
## 0.008771930 0.009900990 0.010752688ggraph(net) +
geom_edge_link(color = "black", alpha = .5,
arrow = grid::arrow(angle = 20,
length = unit(0.10, "in"),
type = "closed"),
start_cap = circle(2, 'mm'),
end_cap = circle(2, 'mm')) +
geom_node_point(fill = "#EFD9D3", color = "black", shape = 21,
size = igraph::closeness(net, mode = "in")*600) +
ggnetwork::theme_blank()## Using "stress" as default layout
#Examining out closeness
close_out <- igraph::closeness(net, mode = "out")
close_out ## V1 V2 V3 V3A V4 V4t
## 0.009803922 0.012658228 0.012820513 0.011627907 0.013333333 0.010638298
## VOT VP MT MSTd/p MSTl PO
## 0.009523810 0.012820513 0.013157895 0.013333333 0.011627907 0.011764706
## LIP PIP VIP DP 7a FST
## 0.014285714 0.010101010 0.014084507 0.011904762 0.012195122 0.013333333
## PITd PITv CITd CITv AITd AITv
## 0.011235955 0.011627907 0.009803922 0.010416667 0.010638298 0.010000000
## STPp STPa TF TH FEF 46
## 0.011363636 0.010101010 0.013698630 0.012048193 0.014084507 0.014705882
## 3a 3b 1 2 5 Ri
## 0.009523810 0.007352941 0.009708738 0.009900990 0.010752688 0.008474576
## SII 7b 4 6 SMA Ig
## 0.010101010 0.011494253 0.009708738 0.010989011 0.010752688 0.008403361
## Id 35 36
## 0.007299270 0.006250000 0.006250000ggraph(net) +
geom_edge_link(color = "black", alpha = .5,
arrow = grid::arrow(angle = 20,
length = unit(0.10, "in"),
type = "closed"),
start_cap = circle(2, 'mm'),
end_cap = circle(2, 'mm')) +
geom_node_point(fill = "#EFD9D3", color = "black", shape = 21,
size = igraph::closeness(net, mode = "out")*10) +
ggnetwork::theme_blank()## Using "stress" as default layout
#betweenness (number of shortest paths through node)
bet_d <- igraph::betweenness(net, directed = T)
summary(bet_d) #number of shortest paths through node## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 10.42 26.70 50.53 53.07 346.73ggraph(net) +
geom_edge_link(color = "black", alpha = .5,
arrow = grid::arrow(angle = 20,
length = unit(0.10, "in"),
type = "closed"),
start_cap = circle(2, 'mm'),
end_cap = circle(2, 'mm')) +
geom_node_point(fill = "#EFD9D3", color = "black", shape = 21,
size = igraph::betweenness(net, directed = T)/50) +
ggnetwork::theme_blank()## Using "stress" as default layout
#edge betweenness (importance of a particular relationship)
eb_n <- edge.betweenness(net, directed = T)
summary(eb_n) #importance of a particular relationship## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.367 3.809 6.316 9.188 11.671 83.410ggraph(net) +
geom_edge_link(color = "black", alpha = .5,
arrow = grid::arrow(angle = 20,
length = unit(0.10, "in"),
type = "closed"),
start_cap = circle(2, 'mm'),
end_cap = circle(2, 'mm'),
aes(width = edge.betweenness(net, directed = T))) +
scale_edge_width(range = c(0.15, 2)) +
geom_node_point(fill = "#EFD9D3", color = "black", shape = 21) +
ggnetwork::theme_blank() + theme(legend.position = "none")## Using "stress" as default layout
#eigenvector centrality (importance based on importance of connected nodes). (eigenvector centrality is a measure of the influence of a node in a network. It takes into account both the number of connections a node has and the importance of the nodes that it is connected to)
evc_dir <- eigen_centrality(net, directed = T)$vector
summary(evc_dir) ## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.04891 0.23282 0.39451 0.47979 0.74847 1.00000evc_undir <- eigen_centrality(net, directed = F)$vector
summary(evc_undir)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0326 0.2282 0.3470 0.4600 0.7652 1.0000ggraph(net) +
geom_edge_link(color = "black", alpha = .5) +
geom_node_point(fill = "#EFD9D3", color = "black", shape = 21,
size = igraph::eigen_centrality(net)$vector*10) +
ggnetwork::theme_blank()## Using "stress" as default layout
#graph strength (sum of the weights of edges connected to the node (can also calcuate manually)
gs <- graph.strength(net, mode = "all")
gs## V1 V2 V3 V3A V4 V4t VOT VP MT MSTd/p MSTl
## 16 28 28 25 40 17 10 27 32 33 19
## PO LIP PIP VIP DP 7a FST PITd PITv CITd CITv
## 28 38 16 40 20 24 35 13 20 9 16
## AITd AITv STPp STPa TF TH FEF 46 3a 3b 1
## 14 12 20 9 29 21 38 36 12 8 15
## 2 5 Ri SII 7b 4 6 SMA Ig Id 35
## 20 20 8 23 22 17 20 16 11 7 6
## 36
## 8ggraph(net) +
geom_edge_link0(color = "black", alpha = .5) +
geom_node_point(fill = "#EFD9D3", color = "black",
shape = 21,
size = graph.strength(net, mode = "all")/5) +
ggnetwork::theme_blank()## Using "stress" as default layout
#Whole network centrality (degree distribution)
in_deg <- igraph::degree(net, mode="in")
table(in_deg)## in_deg
## 3 4 5 6 7 8 9 10 11 12 13 14 15 16 18 20
## 2 4 2 3 2 5 3 5 2 3 3 2 1 3 3 2df <- as.data.frame(in_deg)
df$out_deg <- igraph::degree(net, mode="out")
table(df$out_deg)##
## 2 4 5 6 8 9 10 12 13 14 15 16 17 20
## 2 3 5 2 8 2 8 1 2 2 1 1 3 5df$total_deg <- df$in_deg + df$out_deg
table(df$total_deg)##
## 6 7 8 9 10 11 12 13 14 15 16 17 19 20 21 22 23 24 25 27 28 29 32 33 35 36
## 1 1 3 2 1 1 2 1 1 1 4 2 1 6 1 1 1 1 1 1 3 1 1 1 1 1
## 38 40
## 2 2ind_plot <- ggplot(df, aes(x=in_deg)) +
geom_histogram(colour="black", fill="#DFE6EB", binwidth = 0.5) +
scale_x_continuous(limits = c(-1, 15), expand = c(0, 0)) +
scale_y_continuous(limits = c(0, 25), expand = c(0, 0)) +
xlab("In Degree") + ylab("Count") + theme_classic()
ind_plot## Warning: Removed 8 rows containing non-finite values (`stat_bin()`).## Warning: Removed 2 rows containing missing values (`geom_bar()`).
outd_plot <- ggplot(df, aes(x=out_deg)) +
geom_histogram(colour="black", fill="#DFE6EB", binwidth = 0.5) +
scale_x_continuous(limits = c(-1, 15), expand = c(0, 0)) +
scale_y_continuous(limits = c(0, 25), expand = c(0, 0)) +
xlab("Out Degree") + ylab("Count") + theme_classic()
outd_plot## Warning: Removed 9 rows containing non-finite values (`stat_bin()`).
## Removed 2 rows containing missing values (`geom_bar()`).
totd_plot <- ggplot(df, aes(x=total_deg)) +
geom_histogram(colour="black", fill="#DFE6EB", binwidth = 0.5) +
scale_x_continuous(limits = c(-1, 15), expand = c(0, 0)) +
scale_y_continuous(limits = c(0, 25), expand = c(0, 0)) +
xlab("Total Degree") + ylab("Count") + theme_classic()
totd_plot## Warning: Removed 31 rows containing non-finite values (`stat_bin()`).
## Removed 2 rows containing missing values (`geom_bar()`).
#degree centralization####
cent_deg <- centralization.degree(net, mode = "all")$centralization
round(cent_deg, digits = 3)## [1] 0.226#closeness centralization####
cent_clo <- centralization.closeness(net, mode = "all")$centralization
round(cent_clo, digits = 3)## [1] 0.281#betweenness centralization####
cent_bet <- centralization.betweenness(net)$centralization
round(cent_bet, digits = 3)## [1] 0.16#eigenvector centralization####
cent_evc <- centralization.evcent(net)$centralization
round(cent_evc, digits = 3)## [1] 0.565#We will need to draw conclusions from this in the paper
########MOVING ON TO DYIADS#########
#### Dyads####
igraph::dyad_census(net)## $mut
## [1] 208
##
## $asym
## [1] 47
##
## $null
## [1] 735igraph::reciprocity(net)## [1] 0.8984881igraph::which_mutual(net)## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [13] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE
## [25] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
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## [73] TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
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## [157] TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [169] TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
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## [229] TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [241] TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [253] FALSE TRUE TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
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## [289] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [301] FALSE TRUE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE
## [313] FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
## [325] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
## [337] TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
## [349] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE FALSE
## [361] FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [373] TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE
## [385] TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
## [397] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [409] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE
## [421] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE
## [433] TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [445] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [457] FALSE TRUE TRUE TRUE TRUE TRUE TRUEigraph::triad_census(net)## [1] 5875 1135 5084 27 26 21 212 305 3 1 951 5 16 12 143
## [16] 374#Transitivity (local) (In network analysis, transitivity refers to the tendency for nodes in a network to be connected to other nodes that are also connected to each other. In other words, if node A is connected to node B, and node B is connected to node C, then there is a high likelihood that node A is also connected to node C)
trans <- igraph::transitivity(net, type = "local", isolates = "zero")
summary(trans)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.2667 0.4381 0.6000 0.5752 0.6667 1.0000#global
trans <- igraph::transitivity(net, type = "global")
trans## [1] 0.5187266#### structural holes####
# efficiency####
global_efficiency(net)## [1] 0.5606734local_efficiency(net)## V1 V2 V3 V3A V4 V4t VOT VP
## 0.9017857 0.8031746 0.8159341 0.8557692 0.6462719 0.8777778 0.7500000 0.7930403
## MT MSTd/p MSTl PO LIP PIP VIP DP
## 0.8145833 0.7653186 0.8136364 0.7992063 0.7188596 0.8125000 0.6638889 0.8106061
## 7a FST PITd PITv CITd CITv AITd AITv
## 0.6841270 0.7285575 0.6904762 0.7184343 0.7666667 0.7500000 0.7222222 0.8095238
## STPp STPa TF TH FEF 46 3a 3b
## 0.6759259 0.8500000 0.6083333 0.6611111 0.6620130 0.5885965 0.8833333 1.0000000
## 1 2 5 Ri SII 7b 4 6
## 0.8571429 0.7818182 0.7606061 0.8750000 0.6702991 0.7350427 0.8000000 0.7954545
## SMA Ig Id 35 36
## 0.8571429 0.6666667 0.7500000 0.5763889 0.5500000average_local_efficiency(net)## [1] 0.7581608# constraint####
igraph::constraint(net)## V1 V2 V3 V3A V4 V4t VOT VP
## 0.2606743 0.1908647 0.1817101 0.1964884 0.1329333 0.2305540 0.2673729 0.1692699
## MT MSTd/p MSTl PO LIP PIP VIP DP
## 0.1796758 0.1543983 0.1963156 0.1830402 0.1361632 0.2244618 0.1345924 0.1850261
## 7a FST PITd PITv CITd CITv AITd AITv
## 0.1448434 0.1440908 0.2074931 0.1866928 0.2886026 0.2344980 0.2091123 0.2953937
## STPp STPa TF TH FEF 46 3a 3b
## 0.1751029 0.3218064 0.1314761 0.1952027 0.1338407 0.1168105 0.3517794 0.4661943
## 1 2 5 Ri SII 7b 4 6
## 0.3084688 0.2659752 0.2335999 0.3935846 0.2115280 0.2107918 0.2507298 0.2351460
## SMA Ig Id 35 36
## 0.2564124 0.3093622 0.4032511 0.3400474 0.2313766#### density####
den <- igraph::edge_density(net)
den## [1] 0.2338384net_1 <- igraph::simplify(net)
net_1 <- igraph::undirected_graph(net)
is_simple(net)## [1] TRUEis_directed(net)## [1] TRUEsm50 <- sir(graph = sim_und_net, beta = 10, gamma = 5)
plot(sm50, comp = "NS")
plot(sm50, comp = "NI")
plot(sm50, comp = "NR")
Model
model2 <- ergm(net_sna ~ edges+mutual+nodefactor("vertex.names")+nodematch("vertex.names")) ## Observed statistic(s) nodematch.vertex.names are at their smallest attainable values. Their coefficients will be fixed at -Inf.## Starting maximum pseudolikelihood estimation (MPLE):## Evaluating the predictor and response matrix.## Maximizing the pseudolikelihood.## Finished MPLE.## Starting Monte Carlo maximum likelihood estimation (MCMLE):## Iteration 1 of at most 60:## Optimizing with step length 1.0000.## The log-likelihood improved by 1.4602.## Estimating equations are not within tolerance region.## Iteration 2 of at most 60:## Optimizing with step length 1.0000.## The log-likelihood improved by 0.4495.## Estimating equations are not within tolerance region.## Iteration 3 of at most 60:## Optimizing with step length 1.0000.## The log-likelihood improved by 0.7692.## Estimating equations are not within tolerance region.## Iteration 4 of at most 60:## Optimizing with step length 1.0000.## The log-likelihood improved by 1.2241.## Estimating equations are not within tolerance region.## Estimating equations did not move closer to tolerance region more than 1 time(s) in 4 steps; increasing sample size.## Iteration 5 of at most 60:## Optimizing with step length 1.0000.## The log-likelihood improved by 1.0306.## Estimating equations are not within tolerance region.## Iteration 6 of at most 60:## Optimizing with step length 1.0000.## The log-likelihood improved by 0.8776.## Estimating equations are not within tolerance region.## Iteration 7 of at most 60:## Optimizing with step length 1.0000.## The log-likelihood improved by 0.5140.## Estimating equations are not within tolerance region.## Iteration 8 of at most 60:## Optimizing with step length 1.0000.## The log-likelihood improved by 0.1389.## Estimating equations are not within tolerance region.## Iteration 9 of at most 60:## Optimizing with step length 1.0000.## The log-likelihood improved by 0.2541.## Estimating equations are not within tolerance region.## Iteration 10 of at most 60:## Optimizing with step length 1.0000.## The log-likelihood improved by 0.5691.## Estimating equations are not within tolerance region.## Estimating equations did not move closer to tolerance region more than 1 time(s) in 4 steps; increasing sample size.## Iteration 11 of at most 60:## Optimizing with step length 1.0000.## The log-likelihood improved by 0.6734.## Estimating equations are not within tolerance region.## Iteration 12 of at most 60:## Optimizing with step length 1.0000.## The log-likelihood improved by 0.4096.## Estimating equations are not within tolerance region.## Iteration 13 of at most 60:## Optimizing with step length 1.0000.## The log-likelihood improved by 0.7088.## Estimating equations are not within tolerance region.## Estimating equations did not move closer to tolerance region more than 1 time(s) in 4 steps; increasing sample size.## Iteration 14 of at most 60:## Optimizing with step length 1.0000.## The log-likelihood improved by 0.8582.## Estimating equations are not within tolerance region.## Iteration 15 of at most 60:## Optimizing with step length 1.0000.## The log-likelihood improved by 0.1647.## Estimating equations are not within tolerance region.## Iteration 16 of at most 60:## Optimizing with step length 1.0000.## The log-likelihood improved by 0.1177.## Estimating equations are not within tolerance region.## Iteration 17 of at most 60:## Optimizing with step length 1.0000.## The log-likelihood improved by 0.0610.## Convergence test p-value: 0.6092. Not converged with 99% confidence; increasing sample size.
## Iteration 18 of at most 60:
## Optimizing with step length 1.0000.
## The log-likelihood improved by 0.0509.
## Convergence test p-value: 0.0378. Not converged with 99% confidence; increasing sample size.
## Iteration 19 of at most 60:
## Optimizing with step length 1.0000.
## The log-likelihood improved by 0.0458.
## Convergence test p-value: 0.3457. Not converged with 99% confidence; increasing sample size.
## Iteration 20 of at most 60:
## Optimizing with step length 1.0000.
## The log-likelihood improved by 0.0491.
## Convergence test p-value: 0.0019. Converged with 99% confidence.
## Finished MCMLE.
## Evaluating log-likelihood at the estimate. Fitting the dyad-independent submodel...
## Bridging between the dyad-independent submodel and the full model...
## Setting up bridge sampling...
## Using 16 bridges: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 .
## Bridging finished.
## This model was fit using MCMC. To examine model diagnostics and check
## for degeneracy, use the mcmc.diagnostics() function.summary(model2) ## Call:
## ergm(formula = net_sna ~ edges + mutual + nodefactor("vertex.names") +
## nodematch("vertex.names"))
##
## Monte Carlo Maximum Likelihood Results:
##
## Estimate Std. Error MCMC % z value Pr(>|z|)
## edges -3.81693 0.46685 0 -8.176 < 1e-04 ***
## mutual 5.36380 0.30323 0 17.689 < 1e-04 ***
## nodefactor.vertex.names.2 0.21371 0.29745 0 0.718 0.47248
## nodefactor.vertex.names.35 -0.59182 0.38934 0 -1.520 0.12850
## nodefactor.vertex.names.36 -0.41540 0.36572 0 -1.136 0.25603
## nodefactor.vertex.names.3a -0.14342 0.33278 0 -0.431 0.66648
## nodefactor.vertex.names.3b -0.40753 0.36148 0 -1.127 0.25956
## nodefactor.vertex.names.4 0.09695 0.30732 0 0.315 0.75240
## nodefactor.vertex.names.46 0.74086 0.27748 0 2.670 0.00759 **
## nodefactor.vertex.names.5 0.22065 0.30130 0 0.732 0.46397
## nodefactor.vertex.names.6 0.21087 0.29204 0 0.722 0.47025
## nodefactor.vertex.names.7a 0.36720 0.29025 0 1.265 0.20582
## nodefactor.vertex.names.7b 0.28963 0.29492 0 0.982 0.32608
## nodefactor.vertex.names.AITd -0.04356 0.31845 0 -0.137 0.89119
## nodefactor.vertex.names.AITv -0.14901 0.32224 0 -0.462 0.64379
## nodefactor.vertex.names.CITd -0.33869 0.35410 0 -0.957 0.33882
## nodefactor.vertex.names.CITv 0.06653 0.31511 0 0.211 0.83280
## nodefactor.vertex.names.DP 0.21976 0.28876 0 0.761 0.44663
## nodefactor.vertex.names.FEF 0.81567 0.28455 0 2.866 0.00415 **
## nodefactor.vertex.names.FST 0.71754 0.28327 0 2.533 0.01131 *
## nodefactor.vertex.names.Id -0.50287 0.37540 0 -1.340 0.18039
## nodefactor.vertex.names.Ig -0.20516 0.32988 0 -0.622 0.53398
## nodefactor.vertex.names.LIP 0.79719 0.28316 0 2.815 0.00487 **
## nodefactor.vertex.names.MSTd/p 0.66719 0.28370 0 2.352 0.01868 *
## nodefactor.vertex.names.MSTl 0.18403 0.29305 0 0.628 0.53002
## nodefactor.vertex.names.MT 0.62774 0.29058 0 2.160 0.03075 *
## nodefactor.vertex.names.PIP 0.05027 0.30721 0 0.164 0.87002
## nodefactor.vertex.names.PITd -0.09266 0.31929 0 -0.290 0.77166
## nodefactor.vertex.names.PITv 0.21297 0.30382 0 0.701 0.48332
## nodefactor.vertex.names.PO 0.49360 0.28748 0 1.717 0.08599 .
## nodefactor.vertex.names.Ri -0.42381 0.36636 0 -1.157 0.24735
## nodefactor.vertex.names.SII 0.33030 0.29450 0 1.122 0.26205
## nodefactor.vertex.names.SMA 0.04136 0.30996 0 0.133 0.89385
## nodefactor.vertex.names.STPa -0.33690 0.35404 0 -0.952 0.34131
## nodefactor.vertex.names.STPp 0.20808 0.29704 0 0.700 0.48362
## nodefactor.vertex.names.TF 0.53795 0.28146 0 1.911 0.05597 .
## nodefactor.vertex.names.TH 0.24948 0.28977 0 0.861 0.38926
## nodefactor.vertex.names.V1 0.05592 0.30985 0 0.180 0.85679
## nodefactor.vertex.names.V2 0.50450 0.29295 0 1.722 0.08505 .
## nodefactor.vertex.names.V3 0.49391 0.28750 0 1.718 0.08581 .
## nodefactor.vertex.names.V3A 0.40164 0.30147 0 1.332 0.18278
## nodefactor.vertex.names.V4 0.84787 0.28462 0 2.979 0.00289 **
## nodefactor.vertex.names.V4t 0.09077 0.30693 0 0.296 0.76744
## nodefactor.vertex.names.VIP 0.85876 0.27909 0 3.077 0.00209 **
## nodefactor.vertex.names.VOT -0.25328 0.34728 0 -0.729 0.46580
## nodefactor.vertex.names.VP 0.46892 0.29170 0 1.608 0.10794
## nodematch.vertex.names -Inf 0.00000 0 -Inf < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Warning: The following terms have infinite coefficient estimates:
## nodematch.vertex.namesplogis(coef(model2)[['edges']])## [1] 0.02152185This output is the result of a Monte Carlo Maximum Likelihood analysis of an Exponential Random Graph Model (ERGM) fitted to a social network data. The ERGM is a type of statistical model used to explain the formation and structure of social networks by modeling the probability of observed network configurations as a function of certain network features or characteristics. The model formula in this case is net_sna ~ edges + mutual + nodefactor(“vertex.names”) + nodematch(“vertex.names”). Here, net_sna is the dependent variable representing the observed network configuration, and the independent variables include edges (the total number of edges in the network), mutual (the number of mutual ties, i.e., edges between pairs of nodes that are reciprocated), nodefactor(“vertex.names”) (a set of dummy variables representing the node names or labels), and nodematch(“vertex.names”) (a set of nodal covariate terms representing the similarity between nodes based on their names or labels). The Monte Carlo Maximum Likelihood (MCMLE) algorithm is a computational method used to estimate the model parameters by simulating the network configurations and calculating the likelihood of the observed data given the model parameters. The estimates of the model parameters and their standard errors are reported in the output. In this case, the output shows that edges and mutual have significant negative and positive effects, respectively, on the probability of observing a certain network configuration. Some of the dummy variables representing the node names or labels (nodefactor(“vertex.names”)) and the nodal covariate terms (nodematch(“vertex.names”)) are also found to be significant predictors of the network structure. The z-value and p-value columns show the significance of each coefficient estimate. The output also shows that the model has a good fit to the data, as indicated by the low p-values and high z-values of the coefficient estimates.
gof.model1_btergm <- btergm::gof(model2, nsim = 100)##
## Starting GOF assessment on a single computing core....##
## No 'target' network(s) provided. Using networks on the left-hand side of the model formula as observed networks.## Simulating 100 networks from the following formula:
## net_sna ~ edges + mutual + nodefactor("vertex.names") + nodematch("vertex.names")## One network from which simulations are drawn was provided.## Processing statistic: Dyad-wise shared partners## Processing statistic: Edge-wise shared partners## Processing statistic: Degree## Processing statistic: Indegree## Processing statistic: Geodesic distances## Processing statistic: Tie prediction## Processing statistic: Modularity (walktrap)gof.model1_btergm## Dyad-wise shared partners## obs sim: mean median min max Pr(>z)
## 0 595 273.13 270.0 182 376 0.0209 *
## 1 355 404.78 403.5 295 506 0.7209
## 2 232 368.47 369.0 282 438 0.3274
## 3 189 290.87 290.5 231 350 0.4648
## 4 142 217.95 219.5 169 259 0.5857
## 5 116 152.82 153.0 112 197 0.7916
## 6 101 103.70 102.0 64 143 0.9845
## 7 74 67.56 66.5 38 115 0.9631
## 8 54 43.29 43.0 16 81 0.9387
## 9 43 26.70 25.5 6 51 0.9069
## 10 39 16.60 15.0 1 40 0.8723
## 11 15 8.04 7.0 0 20 0.9602
## 12 18 3.80 3.0 0 13 0.9188
## 13 6 1.56 1.0 0 6 0.9746
## 14 1 0.53 0.0 0 5 0.9973
## 15 0 0.18 0.0 0 3 0.9990
## 16 0 0.02 0.0 0 1 0.9999
## 17 0 0.00 0.0 0 0 1.0000##
## Note: Small p-values indicate a significant difference
## between simulations and observed network(s).## Edge-wise shared partners## obs sim: mean median min max Pr(>z)
## 0 6 27.68 27.0 13 50 0.4061
## 1 25 54.72 53.0 31 81 0.2548
## 2 32 66.28 66.0 43 94 0.1890
## 3 46 68.56 67.5 46 98 0.3873
## 4 52 66.35 67.0 46 96 0.5824
## 5 66 53.26 54.0 31 76 0.6254
## 6 50 42.23 41.5 27 61 0.7659
## 7 46 30.84 30.5 15 57 0.5613
## 8 40 21.85 21.5 8 36 0.4868
## 9 38 15.34 15.0 3 32 0.3852
## 10 30 9.91 9.0 0 23 0.4414
## 11 11 5.32 4.5 0 16 0.8277
## 12 14 2.78 2.0 0 10 0.6673
## 13 6 1.00 1.0 0 5 0.8481
## 14 1 0.38 0.0 0 3 0.9810
## 15 0 0.11 0.0 0 2 0.9966
## 16 0 0.02 0.0 0 1 0.9994
## 17 0 0.00 0.0 0 0 1.0000##
## Note: Small p-values indicate a significant difference
## between simulations and observed network(s).## Degree## obs sim: mean median min max Pr(>z)
## 0 0 0.04 0.0 0 1 0.97028
## 1 0 0.26 0.0 0 2 0.80867
## 2 0 0.76 1.0 0 3 0.47907
## 3 0 1.36 1.0 0 4 0.20530
## 4 4 2.10 2.0 0 6 0.07681 .
## 5 2 2.49 2.0 0 6 0.64814
## 6 4 2.56 2.5 0 6 0.17989
## 7 1 3.02 3.0 0 7 0.05994 .
## 8 5 3.00 3.0 0 9 0.06251 .
## 9 1 2.65 3.0 0 6 0.12437
## 10 4 3.00 3.0 0 7 0.35169
## 11 4 2.96 3.0 0 7 0.33276
## 12 3 2.93 3.0 0 8 0.94802
## 13 3 2.67 2.0 0 7 0.75859
## 14 2 2.55 2.0 0 9 0.60849
## 15 3 1.87 2.0 0 5 0.29262
## 16 1 1.49 1.0 0 6 0.64814
## 17 2 1.58 1.0 0 6 0.69568
## 18 0 1.65 2.0 0 5 0.12437
## 19 1 1.34 1.0 0 5 0.75151
## 20 3 1.40 1.0 0 6 0.13619
## 21 1 1.09 1.0 0 4 0.93320
## 22 1 0.98 1.0 0 3 0.98514
## 23 0 0.58 0.0 0 2 0.58908
## 24 0 0.32 0.0 0 2 0.76569
## 25 0 0.19 0.0 0 1 0.85955
## 26 0 0.10 0.0 0 1 0.92580
## 27 0 0.06 0.0 0 1 0.95544
## 28 0 0.00 0.0 0 0 1.00000##
## Note: Small p-values indicate a significant difference
## between simulations and observed network(s).## Indegree## obs sim: mean median min max Pr(>z)
## 0 0 0.08 0 0 1 0.94408
## 1 0 0.52 0 0 3 0.64842
## 2 0 1.30 1 0 4 0.25433
## 3 2 1.82 2 0 5 0.87459
## 4 4 2.83 3 0 6 0.30494
## 5 2 2.99 3 0 7 0.38535
## 6 3 2.78 3 0 7 0.84703
## 7 2 3.27 3 0 8 0.26546
## 8 5 2.80 2 0 7 0.05372 .
## 9 3 3.34 3 0 8 0.76561
## 10 5 2.93 3 0 7 0.06951 .
## 11 2 3.15 3 0 8 0.31328
## 12 3 2.67 3 0 8 0.77231
## 13 3 2.25 2 0 7 0.51078
## 14 2 1.94 2 0 6 0.95804
## 15 1 1.70 2 0 5 0.53935
## 16 3 1.40 1 0 4 0.16063
## 17 0 1.61 1 0 5 0.15803
## 18 3 1.34 1 0 4 0.14552
## 19 0 1.44 1 0 4 0.20671
## 20 2 1.04 1 0 3 0.39992
## 21 0 0.71 1 0 3 0.53358
## 22 0 0.59 0 0 3 0.60492
## 23 0 0.24 0 0 1 0.83332
## 24 0 0.14 0 0 1 0.90230
## 25 0 0.09 0 0 1 0.93710
## 26 0 0.03 0 0 1 0.97901
## 27 0 0.00 0 0 0 1.00000##
## Note: Small p-values indicate a significant difference
## between simulations and observed network(s).## Geodesic distances## obs sim: mean median min max Pr(>z)
## 1 463 466.63 464.0 430 526 0.9938
## 2 928 1267.92 1270.0 1196 1322 0.4687
## 3 445 235.06 232.5 160 324 0.6545
## 4 120 2.45 1.0 0 21 0.8022
## 5 24 0.02 0.0 0 1 0.9592
## 6 0 0.00 0.0 0 0 1.0000
## Inf 0 7.92 0.0 0 88 0.9865##
## Note: Small p-values indicate a significant difference
## between simulations and observed network(s).## ## ROC model ROC random PR model PR random
## 1 0.7306542 0.5302514 0.4877421 0.2506736## Modularity (walktrap)## Observed:## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.3316 0.3316 0.3316 0.3316 0.3316 0.3316##
## Simulated:## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0451 0.1064 0.1261 0.1247 0.1453 0.1875plot(gof.model1_btergm)
Based on the tables and the graphs, we can see that models simulation of
goodness of fit for edge-wise shared partners and geodesic distances the
performance of simulations is not following the distribution of the
model. However, degree goodness of fit is a good performance estimator,
as it follows the distribution of the model.
mcmc.diagnostics(model2,burnin = 1000, thin = 10)
## Sample statistics summary:
##
## Iterations = 1699840:33886208
## Thinning interval = 8192
## Number of chains = 1
## Sample size per chain = 3930
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges -0.242494 24.228 0.38648 0.71639
## mutual -0.170483 12.190 0.19445 0.37114
## nodefactor.vertex.names.2 -0.024682 5.247 0.08370 0.15306
## nodefactor.vertex.names.35 0.119084 3.070 0.04898 0.06460
## nodefactor.vertex.names.36 -0.071501 3.502 0.05586 0.08951
## nodefactor.vertex.names.3a -0.109924 4.198 0.06697 0.11136
## nodefactor.vertex.names.3b -0.048855 3.523 0.05620 0.09419
## nodefactor.vertex.names.4 -0.045802 4.776 0.07619 0.13605
## nodefactor.vertex.names.46 0.007888 6.061 0.09669 0.19379
## nodefactor.vertex.names.5 -0.292366 5.044 0.08045 0.15379
## nodefactor.vertex.names.6 0.288804 5.124 0.08173 0.15194
## nodefactor.vertex.names.7a -0.342494 5.442 0.08680 0.16534
## nodefactor.vertex.names.7b -0.124682 5.311 0.08472 0.15627
## nodefactor.vertex.names.AITd 0.102290 4.542 0.07245 0.13079
## nodefactor.vertex.names.AITv -0.081170 4.261 0.06797 0.11633
## nodefactor.vertex.names.CITd 0.162595 3.796 0.06055 0.09749
## nodefactor.vertex.names.CITv -0.134097 4.707 0.07509 0.13871
## nodefactor.vertex.names.DP 0.001527 5.152 0.08219 0.14041
## nodefactor.vertex.names.FEF -0.755471 5.975 0.09531 0.18179
## nodefactor.vertex.names.FST 0.111959 5.873 0.09368 0.18445
## nodefactor.vertex.names.Id 0.078117 3.377 0.05387 0.08340
## nodefactor.vertex.names.Ig -0.112723 4.047 0.06456 0.10951
## nodefactor.vertex.names.LIP -0.069975 5.974 0.09530 0.18519
## nodefactor.vertex.names.MSTd/p -0.188295 5.942 0.09479 0.18861
## nodefactor.vertex.names.MSTl -0.135623 4.989 0.07958 0.13994
## nodefactor.vertex.names.MT -0.362087 5.768 0.09202 0.18146
## nodefactor.vertex.names.PIP -0.082443 4.797 0.07652 0.13796
## nodefactor.vertex.names.PITd -0.021120 4.409 0.07033 0.12376
## nodefactor.vertex.names.PITv 0.337150 5.085 0.08111 0.15091
## nodefactor.vertex.names.PO 0.436641 5.856 0.09341 0.19153
## nodefactor.vertex.names.Ri 0.065140 3.521 0.05617 0.08598
## nodefactor.vertex.names.SII -0.059033 5.387 0.08594 0.16333
## nodefactor.vertex.names.SMA 0.175064 4.647 0.07412 0.12902
## nodefactor.vertex.names.STPa -0.056489 3.708 0.05915 0.09311
## nodefactor.vertex.names.STPp 0.305598 5.076 0.08097 0.14240
## nodefactor.vertex.names.TF -0.414758 5.769 0.09203 0.19391
## nodefactor.vertex.names.TH 0.516285 5.339 0.08516 0.16078
## nodefactor.vertex.names.V1 0.031043 4.699 0.07496 0.13010
## nodefactor.vertex.names.V2 -0.156997 5.640 0.08997 0.17964
## nodefactor.vertex.names.V3 0.513486 5.803 0.09257 0.18039
## nodefactor.vertex.names.V3A -0.267684 5.402 0.08617 0.15791
## nodefactor.vertex.names.V4 0.474046 6.034 0.09626 0.18759
## nodefactor.vertex.names.V4t 0.159796 4.807 0.07668 0.12752
## nodefactor.vertex.names.VIP -0.341221 5.980 0.09540 0.19495
## nodefactor.vertex.names.VOT -0.257506 3.746 0.05975 0.09274
## nodefactor.vertex.names.VP 0.149873 5.548 0.08851 0.16701
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -46 -17 -1 16.00 48
## mutual -24 -9 -1 8.00 25
## nodefactor.vertex.names.2 -10 -4 0 3.00 11
## nodefactor.vertex.names.35 -5 -2 0 2.00 7
## nodefactor.vertex.names.36 -6 -2 0 2.00 7
## nodefactor.vertex.names.3a -8 -3 0 3.00 9
## nodefactor.vertex.names.3b -6 -2 0 2.00 7
## nodefactor.vertex.names.4 -9 -3 0 3.00 10
## nodefactor.vertex.names.46 -11 -4 0 4.00 12
## nodefactor.vertex.names.5 -10 -4 0 3.00 10
## nodefactor.vertex.names.6 -9 -3 0 4.00 10
## nodefactor.vertex.names.7a -10 -4 -1 3.00 11
## nodefactor.vertex.names.7b -10 -4 0 3.00 11
## nodefactor.vertex.names.AITd -8 -3 0 3.00 10
## nodefactor.vertex.names.AITv -8 -3 0 3.00 9
## nodefactor.vertex.names.CITd -7 -3 0 3.00 8
## nodefactor.vertex.names.CITv -9 -3 0 3.00 10
## nodefactor.vertex.names.DP -9 -4 0 3.00 11
## nodefactor.vertex.names.FEF -12 -5 -1 3.00 11
## nodefactor.vertex.names.FST -11 -4 0 4.00 11
## nodefactor.vertex.names.Id -6 -2 0 2.00 7
## nodefactor.vertex.names.Ig -7 -3 0 2.00 8
## nodefactor.vertex.names.LIP -12 -4 0 4.00 12
## nodefactor.vertex.names.MSTd/p -12 -4 0 4.00 12
## nodefactor.vertex.names.MSTl -10 -4 0 3.00 10
## nodefactor.vertex.names.MT -12 -4 0 3.75 11
## nodefactor.vertex.names.PIP -9 -3 0 3.00 10
## nodefactor.vertex.names.PITd -8 -3 0 3.00 9
## nodefactor.vertex.names.PITv -9 -3 0 4.00 11
## nodefactor.vertex.names.PO -11 -4 0 4.00 12
## nodefactor.vertex.names.Ri -6 -2 0 2.00 8
## nodefactor.vertex.names.SII -10 -4 0 4.00 11
## nodefactor.vertex.names.SMA -8 -3 0 3.00 9
## nodefactor.vertex.names.STPa -7 -3 0 2.00 8
## nodefactor.vertex.names.STPp -9 -3 0 4.00 11
## nodefactor.vertex.names.TF -12 -4 -1 3.00 11
## nodefactor.vertex.names.TH -10 -3 0 4.00 11
## nodefactor.vertex.names.V1 -9 -3 0 3.00 10
## nodefactor.vertex.names.V2 -11 -4 0 4.00 11
## nodefactor.vertex.names.V3 -11 -3 0 4.00 12
## nodefactor.vertex.names.V3A -10 -4 -1 3.00 11
## nodefactor.vertex.names.V4 -11 -4 1 5.00 12
## nodefactor.vertex.names.V4t -9 -3 0 3.00 10
## nodefactor.vertex.names.VIP -12 -5 0 4.00 11
## nodefactor.vertex.names.VOT -7 -3 0 2.00 7
## nodefactor.vertex.names.VP -10 -4 0 4.00 11
##
##
## Are sample statistics significantly different from observed?
## edges mutual nodefactor.vertex.names.2
## diff. -0.2424936 -0.1704835 -0.02468193
## test stat. -0.3384957 -0.4593508 -0.16125897
## P-val. 0.7349896 0.6459823 0.87188944
## nodefactor.vertex.names.35 nodefactor.vertex.names.36
## diff. 0.11908397 -0.07150127
## test stat. 1.84343637 -0.79883637
## P-val. 0.06526532 0.42438530
## nodefactor.vertex.names.3a nodefactor.vertex.names.3b
## diff. -0.1099237 -0.04885496
## test stat. -0.9871071 -0.51867725
## P-val. 0.3235901 0.60398583
## nodefactor.vertex.names.4 nodefactor.vertex.names.46
## diff. -0.04580153 0.007888041
## test stat. -0.33665911 0.040704603
## P-val. 0.73637389 0.967531392
## nodefactor.vertex.names.5 nodefactor.vertex.names.6
## diff. -0.29236641 0.2888041
## test stat. -1.90106392 1.9007154
## P-val. 0.05729364 0.0573393
## nodefactor.vertex.names.7a nodefactor.vertex.names.7b
## diff. -0.34249364 -0.1246819
## test stat. -2.07140878 -0.7978758
## P-val. 0.03832061 0.4249426
## nodefactor.vertex.names.AITd nodefactor.vertex.names.AITv
## diff. 0.1022901 -0.08117048
## test stat. 0.7821036 -0.69773172
## P-val. 0.4341537 0.48534498
## nodefactor.vertex.names.CITd nodefactor.vertex.names.CITv
## diff. 0.16259542 -0.1340967
## test stat. 1.66785728 -0.9667411
## P-val. 0.09534406 0.3336735
## nodefactor.vertex.names.DP nodefactor.vertex.names.FEF
## diff. 0.001526718 -7.554707e-01
## test stat. 0.010873613 -4.155818e+00
## P-val. 0.991324283 3.241257e-05
## nodefactor.vertex.names.FST nodefactor.vertex.names.Id
## diff. 0.1119593 0.07811705
## test stat. 0.6069890 0.93663302
## P-val. 0.5438582 0.34894736
## nodefactor.vertex.names.Ig nodefactor.vertex.names.LIP
## diff. -0.1127226 -0.06997455
## test stat. -1.0292972 -0.37785930
## P-val. 0.3033400 0.70553512
## nodefactor.vertex.names.MSTd/p nodefactor.vertex.names.MSTl
## diff. -0.1882952 -0.1356234
## test stat. -0.9983384 -0.9691699
## P-val. 0.3181153 0.3324604
## nodefactor.vertex.names.MT nodefactor.vertex.names.PIP
## diff. -0.36208651 -0.08244275
## test stat. -1.99542210 -0.59756899
## P-val. 0.04599686 0.55012756
## nodefactor.vertex.names.PITd nodefactor.vertex.names.PITv
## diff. -0.02111959 0.33715013
## test stat. -0.17064548 2.23416331
## P-val. 0.86450253 0.02547233
## nodefactor.vertex.names.PO nodefactor.vertex.names.Ri
## diff. 0.43664122 0.06513995
## test stat. 2.27980224 0.75760789
## P-val. 0.02261942 0.44868575
## nodefactor.vertex.names.SII nodefactor.vertex.names.SMA
## diff. -0.05903308 0.1750636
## test stat. -0.36142974 1.3569210
## P-val. 0.71777822 0.1748063
## nodefactor.vertex.names.STPa nodefactor.vertex.names.STPp
## diff. -0.05648855 0.30559796
## test stat. -0.60671780 2.14606878
## P-val. 0.54403821 0.03186749
## nodefactor.vertex.names.TF nodefactor.vertex.names.TH
## diff. -0.41475827 0.51628499
## test stat. -2.13888762 3.21112811
## P-val. 0.03244477 0.00132215
## nodefactor.vertex.names.V1 nodefactor.vertex.names.V2
## diff. 0.03104326 -0.1569975
## test stat. 0.23861846 -0.8739510
## P-val. 0.81140145 0.3821449
## nodefactor.vertex.names.V3 nodefactor.vertex.names.V3A
## diff. 0.513486005 -0.2676845
## test stat. 2.846476003 -1.6952115
## P-val. 0.004420606 0.0900353
## nodefactor.vertex.names.V4 nodefactor.vertex.names.V4t
## diff. 0.47404580 0.1597964
## test stat. 2.52704272 1.2530824
## P-val. 0.01150275 0.2101757
## nodefactor.vertex.names.VIP nodefactor.vertex.names.VOT
## diff. -0.34122137 -0.257506361
## test stat. -1.75028542 -2.776689452
## P-val. 0.08006908 0.005491562
## nodefactor.vertex.names.VP (Omni)
## diff. 0.1498728 NA
## test stat. 0.8973934 1.196652e+02
## P-val. 0.3695091 1.771795e-07
##
## Sample statistics cross-correlations:
## edges mutual nodefactor.vertex.names.2
## edges 1.0000000 0.9622021 0.2469479092
## mutual 0.9622021 1.0000000 0.2429011585
## nodefactor.vertex.names.2 0.2469479 0.2429012 1.0000000000
## nodefactor.vertex.names.35 0.1535836 0.1379368 0.0133422958
## nodefactor.vertex.names.36 0.1531311 0.1371533 0.0103765523
## nodefactor.vertex.names.3a 0.1888371 0.1821600 -0.0029656304
## nodefactor.vertex.names.3b 0.1488672 0.1350017 0.0487269302
## nodefactor.vertex.names.4 0.2161350 0.2029051 0.0249784133
## nodefactor.vertex.names.46 0.2672229 0.2502363 0.0464632248
## nodefactor.vertex.names.5 0.2493874 0.2447470 0.0231562744
## nodefactor.vertex.names.6 0.1890395 0.1816035 0.0087856718
## nodefactor.vertex.names.7a 0.2578577 0.2537753 0.0356370667
## nodefactor.vertex.names.7b 0.2382640 0.2300030 0.0165405589
## nodefactor.vertex.names.AITd 0.1988261 0.1799934 0.0133594549
## nodefactor.vertex.names.AITv 0.1866281 0.1737955 0.0292376526
## nodefactor.vertex.names.CITd 0.1693039 0.1553302 -0.0062259319
## nodefactor.vertex.names.CITv 0.2263512 0.2103235 0.0702578378
## nodefactor.vertex.names.DP 0.2142695 0.1994934 0.0423209363
## nodefactor.vertex.names.FEF 0.2717308 0.2737121 0.0185977947
## nodefactor.vertex.names.FST 0.2491168 0.2484098 0.0154530516
## nodefactor.vertex.names.Id 0.1414785 0.1290751 -0.0071300602
## nodefactor.vertex.names.Ig 0.2072040 0.1897622 0.0073242572
## nodefactor.vertex.names.LIP 0.2335749 0.2370474 0.0329509613
## nodefactor.vertex.names.MSTd/p 0.2823596 0.2874968 0.0670754106
## nodefactor.vertex.names.MSTl 0.2296481 0.2241941 0.0814683134
## nodefactor.vertex.names.MT 0.2763258 0.2676439 0.0403797830
## nodefactor.vertex.names.PIP 0.1850881 0.1832836 0.0253822858
## nodefactor.vertex.names.PITd 0.1907866 0.1803007 0.0372187233
## nodefactor.vertex.names.PITv 0.2347109 0.2253754 -0.0230228655
## nodefactor.vertex.names.PO 0.2144851 0.2107027 0.0354146136
## nodefactor.vertex.names.Ri 0.1185407 0.1034656 -0.0130141225
## nodefactor.vertex.names.SII 0.2142922 0.2042350 0.0465441577
## nodefactor.vertex.names.SMA 0.2131611 0.2101963 0.0344192488
## nodefactor.vertex.names.STPa 0.1229990 0.1088797 0.0001768849
## nodefactor.vertex.names.STPp 0.1914172 0.1779564 0.0264296042
## nodefactor.vertex.names.TF 0.2261257 0.2202190 0.0315203744
## nodefactor.vertex.names.TH 0.2334479 0.2161848 0.0379169273
## nodefactor.vertex.names.V1 0.2150118 0.2088596 0.0623292663
## nodefactor.vertex.names.V2 0.2433135 0.2316546 0.0147050817
## nodefactor.vertex.names.V3 0.2298051 0.2232689 0.0200689532
## nodefactor.vertex.names.V3A 0.2522668 0.2399200 0.0553502165
## nodefactor.vertex.names.V4 0.2579613 0.2545628 0.0546556918
## nodefactor.vertex.names.V4t 0.2067899 0.1995692 0.0305421684
## nodefactor.vertex.names.VIP 0.2551025 0.2542918 0.0774681897
## nodefactor.vertex.names.VOT 0.1417486 0.1296544 0.0522702595
## nodefactor.vertex.names.VP 0.2294039 0.2201491 0.0307352907
## nodefactor.vertex.names.35
## edges 0.1535835894
## mutual 0.1379367580
## nodefactor.vertex.names.2 0.0133422958
## nodefactor.vertex.names.35 1.0000000000
## nodefactor.vertex.names.36 -0.0095761388
## nodefactor.vertex.names.3a 0.0140078198
## nodefactor.vertex.names.3b 0.0028905424
## nodefactor.vertex.names.4 -0.0070209181
## nodefactor.vertex.names.46 0.0171808229
## nodefactor.vertex.names.5 0.0296791271
## nodefactor.vertex.names.6 0.0384681089
## nodefactor.vertex.names.7a 0.0034622284
## nodefactor.vertex.names.7b 0.0276319501
## nodefactor.vertex.names.AITd 0.0079957190
## nodefactor.vertex.names.AITv 0.0007390399
## nodefactor.vertex.names.CITd 0.0236898097
## nodefactor.vertex.names.CITv 0.0577358189
## nodefactor.vertex.names.DP 0.0347552347
## nodefactor.vertex.names.FEF 0.0393115202
## nodefactor.vertex.names.FST 0.0215475002
## nodefactor.vertex.names.Id -0.0050942532
## nodefactor.vertex.names.Ig -0.0151207848
## nodefactor.vertex.names.LIP 0.0275937609
## nodefactor.vertex.names.MSTd/p 0.0451007707
## nodefactor.vertex.names.MSTl 0.0465300589
## nodefactor.vertex.names.MT 0.0187447781
## nodefactor.vertex.names.PIP -0.0096842376
## nodefactor.vertex.names.PITd 0.0267693684
## nodefactor.vertex.names.PITv 0.0181967522
## nodefactor.vertex.names.PO 0.0386805405
## nodefactor.vertex.names.Ri -0.0106520204
## nodefactor.vertex.names.SII -0.0061756274
## nodefactor.vertex.names.SMA 0.0292045181
## nodefactor.vertex.names.STPa 0.0028266050
## nodefactor.vertex.names.STPp 0.0442382741
## nodefactor.vertex.names.TF 0.0039527741
## nodefactor.vertex.names.TH 0.0026298615
## nodefactor.vertex.names.V1 0.0128678027
## nodefactor.vertex.names.V2 0.0221699803
## nodefactor.vertex.names.V3 0.0357353728
## nodefactor.vertex.names.V3A 0.0088886920
## nodefactor.vertex.names.V4 0.0378334840
## nodefactor.vertex.names.V4t 0.0265433862
## nodefactor.vertex.names.VIP 0.0384029901
## nodefactor.vertex.names.VOT 0.0295118342
## nodefactor.vertex.names.VP 0.0468788428
## nodefactor.vertex.names.36
## edges 0.1531310729
## mutual 0.1371533064
## nodefactor.vertex.names.2 0.0103765523
## nodefactor.vertex.names.35 -0.0095761388
## nodefactor.vertex.names.36 1.0000000000
## nodefactor.vertex.names.3a -0.0376027077
## nodefactor.vertex.names.3b -0.0053580008
## nodefactor.vertex.names.4 0.0390493371
## nodefactor.vertex.names.46 0.0799505738
## nodefactor.vertex.names.5 0.0150864338
## nodefactor.vertex.names.6 0.0229828495
## nodefactor.vertex.names.7a 0.0445024600
## nodefactor.vertex.names.7b 0.0103051870
## nodefactor.vertex.names.AITd -0.0003401552
## nodefactor.vertex.names.AITv -0.0163733675
## nodefactor.vertex.names.CITd 0.0276613589
## nodefactor.vertex.names.CITv 0.0162175985
## nodefactor.vertex.names.DP 0.0050987229
## nodefactor.vertex.names.FEF 0.0635228069
## nodefactor.vertex.names.FST 0.0030874646
## nodefactor.vertex.names.Id 0.0084783401
## nodefactor.vertex.names.Ig 0.0124160912
## nodefactor.vertex.names.LIP 0.0148105546
## nodefactor.vertex.names.MSTd/p 0.0258836262
## nodefactor.vertex.names.MSTl 0.0005228812
## nodefactor.vertex.names.MT 0.0298406484
## nodefactor.vertex.names.PIP 0.0093162515
## nodefactor.vertex.names.PITd 0.0199645923
## nodefactor.vertex.names.PITv 0.0287715557
## nodefactor.vertex.names.PO 0.0284690804
## nodefactor.vertex.names.Ri 0.0150131846
## nodefactor.vertex.names.SII 0.0116218040
## nodefactor.vertex.names.SMA 0.0397980774
## nodefactor.vertex.names.STPa -0.0386153335
## nodefactor.vertex.names.STPp 0.0191860390
## nodefactor.vertex.names.TF 0.0360887284
## nodefactor.vertex.names.TH 0.0090411035
## nodefactor.vertex.names.V1 0.0124007989
## nodefactor.vertex.names.V2 0.0010939084
## nodefactor.vertex.names.V3 0.0418512581
## nodefactor.vertex.names.V3A 0.0364599051
## nodefactor.vertex.names.V4 0.0173357182
## nodefactor.vertex.names.V4t 0.0139705201
## nodefactor.vertex.names.VIP 0.0036110490
## nodefactor.vertex.names.VOT -0.0200142014
## nodefactor.vertex.names.VP 0.0321621013
## nodefactor.vertex.names.3a
## edges 0.1888370921
## mutual 0.1821600210
## nodefactor.vertex.names.2 -0.0029656304
## nodefactor.vertex.names.35 0.0140078198
## nodefactor.vertex.names.36 -0.0376027077
## nodefactor.vertex.names.3a 1.0000000000
## nodefactor.vertex.names.3b 0.0335689625
## nodefactor.vertex.names.4 0.0249947281
## nodefactor.vertex.names.46 0.0589273085
## nodefactor.vertex.names.5 0.0084468200
## nodefactor.vertex.names.6 -0.0180470129
## nodefactor.vertex.names.7a 0.0186285172
## nodefactor.vertex.names.7b -0.0154895152
## nodefactor.vertex.names.AITd 0.0685429141
## nodefactor.vertex.names.AITv 0.0257392891
## nodefactor.vertex.names.CITd 0.0370874427
## nodefactor.vertex.names.CITv 0.0136141253
## nodefactor.vertex.names.DP 0.0183286910
## nodefactor.vertex.names.FEF 0.0577225366
## nodefactor.vertex.names.FST 0.0266173758
## nodefactor.vertex.names.Id 0.0282683153
## nodefactor.vertex.names.Ig 0.0071501983
## nodefactor.vertex.names.LIP 0.0253778530
## nodefactor.vertex.names.MSTd/p 0.0516625600
## nodefactor.vertex.names.MSTl 0.0218788163
## nodefactor.vertex.names.MT 0.0385040185
## nodefactor.vertex.names.PIP -0.0068200625
## nodefactor.vertex.names.PITd 0.0082897531
## nodefactor.vertex.names.PITv 0.0183099969
## nodefactor.vertex.names.PO 0.0283421406
## nodefactor.vertex.names.Ri 0.0369862105
## nodefactor.vertex.names.SII -0.0009171769
## nodefactor.vertex.names.SMA 0.0161481402
## nodefactor.vertex.names.STPa 0.0004185334
## nodefactor.vertex.names.STPp -0.0049445106
## nodefactor.vertex.names.TF 0.0382919815
## nodefactor.vertex.names.TH 0.0443340926
## nodefactor.vertex.names.V1 0.0279890091
## nodefactor.vertex.names.V2 0.0630245992
## nodefactor.vertex.names.V3 0.0133606120
## nodefactor.vertex.names.V3A 0.0196776428
## nodefactor.vertex.names.V4 -0.0048146753
## nodefactor.vertex.names.V4t 0.0209122000
## nodefactor.vertex.names.VIP 0.0450566214
## nodefactor.vertex.names.VOT 0.0323040153
## nodefactor.vertex.names.VP 0.0348318704
## nodefactor.vertex.names.3b
## edges 0.1488672083
## mutual 0.1350017189
## nodefactor.vertex.names.2 0.0487269302
## nodefactor.vertex.names.35 0.0028905424
## nodefactor.vertex.names.36 -0.0053580008
## nodefactor.vertex.names.3a 0.0335689625
## nodefactor.vertex.names.3b 1.0000000000
## nodefactor.vertex.names.4 0.0097125216
## nodefactor.vertex.names.46 0.0414446513
## nodefactor.vertex.names.5 -0.0184639617
## nodefactor.vertex.names.6 0.0258346350
## nodefactor.vertex.names.7a 0.0032290152
## nodefactor.vertex.names.7b -0.0047598824
## nodefactor.vertex.names.AITd 0.0049722417
## nodefactor.vertex.names.AITv 0.0390863318
## nodefactor.vertex.names.CITd 0.0164263933
## nodefactor.vertex.names.CITv 0.0099786218
## nodefactor.vertex.names.DP 0.0011678044
## nodefactor.vertex.names.FEF 0.0219675744
## nodefactor.vertex.names.FST 0.0318766873
## nodefactor.vertex.names.Id 0.0174962010
## nodefactor.vertex.names.Ig 0.0148749806
## nodefactor.vertex.names.LIP -0.0092553180
## nodefactor.vertex.names.MSTd/p 0.0253690598
## nodefactor.vertex.names.MSTl 0.0171721392
## nodefactor.vertex.names.MT 0.0131299311
## nodefactor.vertex.names.PIP -0.0187463117
## nodefactor.vertex.names.PITd 0.0289329389
## nodefactor.vertex.names.PITv 0.0243467768
## nodefactor.vertex.names.PO 0.0462071006
## nodefactor.vertex.names.Ri -0.0262287485
## nodefactor.vertex.names.SII 0.0206850752
## nodefactor.vertex.names.SMA 0.0368393412
## nodefactor.vertex.names.STPa -0.0289674287
## nodefactor.vertex.names.STPp 0.0289702736
## nodefactor.vertex.names.TF 0.0236825290
## nodefactor.vertex.names.TH 0.0361291704
## nodefactor.vertex.names.V1 0.0219361116
## nodefactor.vertex.names.V2 0.0048651769
## nodefactor.vertex.names.V3 0.0265226346
## nodefactor.vertex.names.V3A 0.0003022737
## nodefactor.vertex.names.V4 0.0331966019
## nodefactor.vertex.names.V4t 0.0094930674
## nodefactor.vertex.names.VIP 0.0296596531
## nodefactor.vertex.names.VOT 0.0178699680
## nodefactor.vertex.names.VP 0.0441332765
## nodefactor.vertex.names.4
## edges 0.2161350218
## mutual 0.2029050595
## nodefactor.vertex.names.2 0.0249784133
## nodefactor.vertex.names.35 -0.0070209181
## nodefactor.vertex.names.36 0.0390493371
## nodefactor.vertex.names.3a 0.0249947281
## nodefactor.vertex.names.3b 0.0097125216
## nodefactor.vertex.names.4 1.0000000000
## nodefactor.vertex.names.46 0.0519336489
## nodefactor.vertex.names.5 0.0273467175
## nodefactor.vertex.names.6 0.0370228375
## nodefactor.vertex.names.7a 0.0360782440
## nodefactor.vertex.names.7b 0.0153469175
## nodefactor.vertex.names.AITd 0.0350238495
## nodefactor.vertex.names.AITv 0.0442519919
## nodefactor.vertex.names.CITd 0.0634086934
## nodefactor.vertex.names.CITv 0.0427533896
## nodefactor.vertex.names.DP 0.0297469714
## nodefactor.vertex.names.FEF 0.0238213873
## nodefactor.vertex.names.FST 0.0128312913
## nodefactor.vertex.names.Id 0.0032511625
## nodefactor.vertex.names.Ig 0.0386799191
## nodefactor.vertex.names.LIP 0.0406670391
## nodefactor.vertex.names.MSTd/p 0.0325525163
## nodefactor.vertex.names.MSTl 0.0604812356
## nodefactor.vertex.names.MT 0.0451512321
## nodefactor.vertex.names.PIP 0.0354936160
## nodefactor.vertex.names.PITd 0.0079426124
## nodefactor.vertex.names.PITv 0.0496286899
## nodefactor.vertex.names.PO -0.0232798804
## nodefactor.vertex.names.Ri -0.0092808073
## nodefactor.vertex.names.SII 0.0113683684
## nodefactor.vertex.names.SMA 0.0403730720
## nodefactor.vertex.names.STPa 0.0290275737
## nodefactor.vertex.names.STPp 0.0085032464
## nodefactor.vertex.names.TF -0.0126599646
## nodefactor.vertex.names.TH 0.0178654491
## nodefactor.vertex.names.V1 0.0663548909
## nodefactor.vertex.names.V2 0.0288416718
## nodefactor.vertex.names.V3 0.0271845911
## nodefactor.vertex.names.V3A 0.0311883265
## nodefactor.vertex.names.V4 0.0166575014
## nodefactor.vertex.names.V4t -0.0279384036
## nodefactor.vertex.names.VIP 0.0552474808
## nodefactor.vertex.names.VOT 0.0442256196
## nodefactor.vertex.names.VP 0.0005472012
## nodefactor.vertex.names.46
## edges 0.26722293
## mutual 0.25023631
## nodefactor.vertex.names.2 0.04646322
## nodefactor.vertex.names.35 0.01718082
## nodefactor.vertex.names.36 0.07995057
## nodefactor.vertex.names.3a 0.05892731
## nodefactor.vertex.names.3b 0.04144465
## nodefactor.vertex.names.4 0.05193365
## nodefactor.vertex.names.46 1.00000000
## nodefactor.vertex.names.5 0.03004794
## nodefactor.vertex.names.6 0.03289622
## nodefactor.vertex.names.7a 0.01306898
## nodefactor.vertex.names.7b 0.04260835
## nodefactor.vertex.names.AITd 0.01486420
## nodefactor.vertex.names.AITv 0.04700494
## nodefactor.vertex.names.CITd 0.02412474
## nodefactor.vertex.names.CITv 0.03194513
## nodefactor.vertex.names.DP 0.03016254
## nodefactor.vertex.names.FEF 0.01409412
## nodefactor.vertex.names.FST 0.02502936
## nodefactor.vertex.names.Id 0.03429810
## nodefactor.vertex.names.Ig 0.03054090
## nodefactor.vertex.names.LIP 0.02166371
## nodefactor.vertex.names.MSTd/p 0.04212959
## nodefactor.vertex.names.MSTl 0.01794637
## nodefactor.vertex.names.MT 0.03367617
## nodefactor.vertex.names.PIP -0.01574333
## nodefactor.vertex.names.PITd 0.04194891
## nodefactor.vertex.names.PITv 0.03958666
## nodefactor.vertex.names.PO -0.02052601
## nodefactor.vertex.names.Ri 0.04404024
## nodefactor.vertex.names.SII 0.03242308
## nodefactor.vertex.names.SMA 0.03451778
## nodefactor.vertex.names.STPa 0.03122015
## nodefactor.vertex.names.STPp 0.04645453
## nodefactor.vertex.names.TF 0.03444114
## nodefactor.vertex.names.TH 0.02118929
## nodefactor.vertex.names.V1 0.04077512
## nodefactor.vertex.names.V2 0.01882031
## nodefactor.vertex.names.V3 0.03698479
## nodefactor.vertex.names.V3A 0.06976639
## nodefactor.vertex.names.V4 0.03716213
## nodefactor.vertex.names.V4t 0.04937510
## nodefactor.vertex.names.VIP 0.01559853
## nodefactor.vertex.names.VOT 0.08153739
## nodefactor.vertex.names.VP 0.02693766
## nodefactor.vertex.names.5
## edges 0.249387417
## mutual 0.244746950
## nodefactor.vertex.names.2 0.023156274
## nodefactor.vertex.names.35 0.029679127
## nodefactor.vertex.names.36 0.015086434
## nodefactor.vertex.names.3a 0.008446820
## nodefactor.vertex.names.3b -0.018463962
## nodefactor.vertex.names.4 0.027346717
## nodefactor.vertex.names.46 0.030047936
## nodefactor.vertex.names.5 1.000000000
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## nodefactor.vertex.names.7a 0.057779618
## nodefactor.vertex.names.7b 0.058407611
## nodefactor.vertex.names.AITd 0.044814045
## nodefactor.vertex.names.AITv 0.044814347
## nodefactor.vertex.names.CITd 0.003693253
## nodefactor.vertex.names.CITv 0.005455826
## nodefactor.vertex.names.DP 0.036805260
## nodefactor.vertex.names.FEF 0.064190590
## nodefactor.vertex.names.FST 0.033707132
## nodefactor.vertex.names.Id -0.004097776
## nodefactor.vertex.names.Ig 0.019383464
## nodefactor.vertex.names.LIP 0.018073239
## nodefactor.vertex.names.MSTd/p 0.101371696
## nodefactor.vertex.names.MSTl 0.061734825
## nodefactor.vertex.names.MT 0.041632783
## nodefactor.vertex.names.PIP 0.024630819
## nodefactor.vertex.names.PITd 0.003510729
## nodefactor.vertex.names.PITv 0.001849827
## nodefactor.vertex.names.PO 0.040844312
## nodefactor.vertex.names.Ri -0.017157336
## nodefactor.vertex.names.SII 0.045347810
## nodefactor.vertex.names.SMA 0.045236149
## nodefactor.vertex.names.STPa 0.031781113
## nodefactor.vertex.names.STPp 0.028166418
## nodefactor.vertex.names.TF 0.016588715
## nodefactor.vertex.names.TH 0.060063188
## nodefactor.vertex.names.V1 0.026060266
## nodefactor.vertex.names.V2 0.037978471
## nodefactor.vertex.names.V3 0.031828639
## nodefactor.vertex.names.V3A 0.048207038
## nodefactor.vertex.names.V4 0.069569051
## nodefactor.vertex.names.V4t 0.028698906
## nodefactor.vertex.names.VIP 0.039414251
## nodefactor.vertex.names.VOT 0.027514002
## nodefactor.vertex.names.VP 0.004976969
## nodefactor.vertex.names.6
## edges 1.890395e-01
## mutual 1.816035e-01
## nodefactor.vertex.names.2 8.785672e-03
## nodefactor.vertex.names.35 3.846811e-02
## nodefactor.vertex.names.36 2.298285e-02
## nodefactor.vertex.names.3a -1.804701e-02
## nodefactor.vertex.names.3b 2.583463e-02
## nodefactor.vertex.names.4 3.702284e-02
## nodefactor.vertex.names.46 3.289622e-02
## nodefactor.vertex.names.5 1.395437e-02
## nodefactor.vertex.names.6 1.000000e+00
## nodefactor.vertex.names.7a 3.285082e-02
## nodefactor.vertex.names.7b 4.984890e-02
## nodefactor.vertex.names.AITd 4.337245e-02
## nodefactor.vertex.names.AITv -3.132793e-04
## nodefactor.vertex.names.CITd 1.277725e-02
## nodefactor.vertex.names.CITv 3.638684e-02
## nodefactor.vertex.names.DP 2.659263e-02
## nodefactor.vertex.names.FEF 2.256877e-03
## nodefactor.vertex.names.FST 1.985117e-02
## nodefactor.vertex.names.Id -3.774506e-04
## nodefactor.vertex.names.Ig 2.228901e-02
## nodefactor.vertex.names.LIP -1.935314e-02
## nodefactor.vertex.names.MSTd/p 1.389102e-02
## nodefactor.vertex.names.MSTl -3.511818e-02
## nodefactor.vertex.names.MT 4.022291e-02
## nodefactor.vertex.names.PIP 2.064440e-02
## nodefactor.vertex.names.PITd 4.562501e-03
## nodefactor.vertex.names.PITv 4.078999e-02
## nodefactor.vertex.names.PO 6.312019e-04
## nodefactor.vertex.names.Ri -8.886651e-03
## nodefactor.vertex.names.SII 2.929320e-02
## nodefactor.vertex.names.SMA 1.894683e-02
## nodefactor.vertex.names.STPa -2.526002e-05
## nodefactor.vertex.names.STPp 1.108905e-02
## nodefactor.vertex.names.TF 1.779527e-02
## nodefactor.vertex.names.TH 2.335568e-03
## nodefactor.vertex.names.V1 2.135073e-02
## nodefactor.vertex.names.V2 1.365302e-02
## nodefactor.vertex.names.V3 7.270293e-02
## nodefactor.vertex.names.V3A 1.841663e-02
## nodefactor.vertex.names.V4 4.089621e-02
## nodefactor.vertex.names.V4t 4.176663e-02
## nodefactor.vertex.names.VIP 1.648982e-02
## nodefactor.vertex.names.VOT -2.946498e-02
## nodefactor.vertex.names.VP 5.668823e-02
## nodefactor.vertex.names.7a
## edges 0.2578576602
## mutual 0.2537752891
## nodefactor.vertex.names.2 0.0356370667
## nodefactor.vertex.names.35 0.0034622284
## nodefactor.vertex.names.36 0.0445024600
## nodefactor.vertex.names.3a 0.0186285172
## nodefactor.vertex.names.3b 0.0032290152
## nodefactor.vertex.names.4 0.0360782440
## nodefactor.vertex.names.46 0.0130689782
## nodefactor.vertex.names.5 0.0577796177
## nodefactor.vertex.names.6 0.0328508200
## nodefactor.vertex.names.7a 1.0000000000
## nodefactor.vertex.names.7b 0.0529672054
## nodefactor.vertex.names.AITd 0.0566230897
## nodefactor.vertex.names.AITv 0.0321392501
## nodefactor.vertex.names.CITd 0.0221021913
## nodefactor.vertex.names.CITv -0.0220133088
## nodefactor.vertex.names.DP 0.0207890017
## nodefactor.vertex.names.FEF 0.0481449543
## nodefactor.vertex.names.FST 0.0619282431
## nodefactor.vertex.names.Id 0.0382113769
## nodefactor.vertex.names.Ig 0.0655670824
## nodefactor.vertex.names.LIP 0.0368890894
## nodefactor.vertex.names.MSTd/p 0.0656658497
## nodefactor.vertex.names.MSTl 0.0350956426
## nodefactor.vertex.names.MT -0.0239467726
## nodefactor.vertex.names.PIP 0.0447167295
## nodefactor.vertex.names.PITd 0.0651723232
## nodefactor.vertex.names.PITv 0.0383842592
## nodefactor.vertex.names.PO 0.0395975930
## nodefactor.vertex.names.Ri 0.0184595147
## nodefactor.vertex.names.SII 0.0207629961
## nodefactor.vertex.names.SMA 0.0125789614
## nodefactor.vertex.names.STPa 0.0009078219
## nodefactor.vertex.names.STPp 0.0435783789
## nodefactor.vertex.names.TF 0.0277006883
## nodefactor.vertex.names.TH 0.0529068829
## nodefactor.vertex.names.V1 0.0123302031
## nodefactor.vertex.names.V2 0.0492156596
## nodefactor.vertex.names.V3 0.0195551776
## nodefactor.vertex.names.V3A 0.0501460271
## nodefactor.vertex.names.V4 0.0235020931
## nodefactor.vertex.names.V4t 0.0284820406
## nodefactor.vertex.names.VIP 0.0182751708
## nodefactor.vertex.names.VOT -0.0033666908
## nodefactor.vertex.names.VP 0.0732281894
## nodefactor.vertex.names.7b
## edges 0.238264016
## mutual 0.230002961
## nodefactor.vertex.names.2 0.016540559
## nodefactor.vertex.names.35 0.027631950
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## nodefactor.vertex.names.3a -0.015489515
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## nodefactor.vertex.names.7a 0.052967205
## nodefactor.vertex.names.7b 1.000000000
## nodefactor.vertex.names.AITd 0.046722445
## nodefactor.vertex.names.AITv 0.029764639
## nodefactor.vertex.names.CITd 0.044838621
## nodefactor.vertex.names.CITv 0.051487742
## nodefactor.vertex.names.DP 0.002341666
## nodefactor.vertex.names.FEF 0.058047672
## nodefactor.vertex.names.FST 0.021093930
## nodefactor.vertex.names.Id 0.027121446
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## nodefactor.vertex.names.MT 0.064766243
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## nodefactor.vertex.names.PITd -0.002829883
## nodefactor.vertex.names.PITv 0.053311542
## nodefactor.vertex.names.PO 0.010434040
## nodefactor.vertex.names.Ri 0.037278304
## nodefactor.vertex.names.SII 0.047975500
## nodefactor.vertex.names.SMA 0.016067151
## nodefactor.vertex.names.STPa 0.004605564
## nodefactor.vertex.names.STPp 0.007144806
## nodefactor.vertex.names.TF 0.015490847
## nodefactor.vertex.names.TH 0.018842226
## nodefactor.vertex.names.V1 0.038165759
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## nodefactor.vertex.names.V3 0.029579637
## nodefactor.vertex.names.V3A 0.024430909
## nodefactor.vertex.names.V4 0.020080040
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## nodefactor.vertex.names.VIP 0.034625087
## nodefactor.vertex.names.VOT -0.004659749
## nodefactor.vertex.names.VP -0.001101832
## nodefactor.vertex.names.AITd
## edges 0.1988261494
## mutual 0.1799934008
## nodefactor.vertex.names.2 0.0133594549
## nodefactor.vertex.names.35 0.0079957190
## nodefactor.vertex.names.36 -0.0003401552
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## nodefactor.vertex.names.7a 0.0566230897
## nodefactor.vertex.names.7b 0.0467224449
## nodefactor.vertex.names.AITd 1.0000000000
## nodefactor.vertex.names.AITv 0.0251935581
## nodefactor.vertex.names.CITd 0.0295909724
## nodefactor.vertex.names.CITv 0.0133788949
## nodefactor.vertex.names.DP 0.0274439525
## nodefactor.vertex.names.FEF 0.0333102130
## nodefactor.vertex.names.FST 0.0310199122
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## nodefactor.vertex.names.LIP 0.0283370313
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## nodefactor.vertex.names.MSTl 0.0075537369
## nodefactor.vertex.names.MT 0.0333346575
## nodefactor.vertex.names.PIP -0.0465735037
## nodefactor.vertex.names.PITd 0.0154478766
## nodefactor.vertex.names.PITv 0.0290330663
## nodefactor.vertex.names.PO 0.0389116517
## nodefactor.vertex.names.Ri 0.0238840501
## nodefactor.vertex.names.SII 0.0223079771
## nodefactor.vertex.names.SMA -0.0161283511
## nodefactor.vertex.names.STPa 0.0093957782
## nodefactor.vertex.names.STPp -0.0158730322
## nodefactor.vertex.names.TF 0.0231435385
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## nodefactor.vertex.names.V3 0.0179376742
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## nodefactor.vertex.names.V4 0.0430460693
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## nodefactor.vertex.names.VIP 0.0328334101
## nodefactor.vertex.names.VOT 0.0402820301
## nodefactor.vertex.names.VP 0.0445562096
## nodefactor.vertex.names.AITv
## edges 0.1866281432
## mutual 0.1737955165
## nodefactor.vertex.names.2 0.0292376526
## nodefactor.vertex.names.35 0.0007390399
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## nodefactor.vertex.names.AITd 0.0251935581
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## nodefactor.vertex.names.MSTl 0.0176458922
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## nodefactor.vertex.names.PIP 0.0098964494
## nodefactor.vertex.names.PITd 0.0124273581
## nodefactor.vertex.names.PITv 0.0364843533
## nodefactor.vertex.names.PO 0.0347669074
## nodefactor.vertex.names.Ri 0.0064937811
## nodefactor.vertex.names.SII -0.0056862988
## nodefactor.vertex.names.SMA 0.0140367099
## nodefactor.vertex.names.STPa 0.0151114907
## nodefactor.vertex.names.STPp -0.0169406397
## nodefactor.vertex.names.TF -0.0054599205
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## nodefactor.vertex.names.VP 0.0205507387
## nodefactor.vertex.names.CITd
## edges 0.1693038914
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## nodefactor.vertex.names.SII -0.0060269192
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## nodefactor.vertex.names.CITv
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## nodefactor.vertex.names.PO 0.021452423
## nodefactor.vertex.names.Ri 0.009154592
## nodefactor.vertex.names.SII 0.023220606
## nodefactor.vertex.names.SMA 0.055353264
## nodefactor.vertex.names.STPa -0.018708768
## nodefactor.vertex.names.STPp 0.024887034
## nodefactor.vertex.names.TF 0.021788875
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## nodefactor.vertex.names.VP 0.028459783
## nodefactor.vertex.names.PITv
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## nodefactor.vertex.names.PITv 1.000000000
## nodefactor.vertex.names.PO 0.078361165
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## nodefactor.vertex.names.SII 0.042212513
## nodefactor.vertex.names.SMA 0.071046765
## nodefactor.vertex.names.STPa -0.022790192
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## nodefactor.vertex.names.VOT -0.023077359
## nodefactor.vertex.names.VP 0.006111454
## nodefactor.vertex.names.PO
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## nodefactor.vertex.names.PIP 0.0243412216
## nodefactor.vertex.names.PITd 0.0214524231
## nodefactor.vertex.names.PITv 0.0783611647
## nodefactor.vertex.names.PO 1.0000000000
## nodefactor.vertex.names.Ri -0.0230916985
## nodefactor.vertex.names.SII 0.0332244125
## nodefactor.vertex.names.SMA 0.0299844867
## nodefactor.vertex.names.STPa 0.0023318884
## nodefactor.vertex.names.STPp 0.0552072132
## nodefactor.vertex.names.TF 0.0310215662
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## nodefactor.vertex.names.V4 0.0057444195
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## nodefactor.vertex.names.VOT -0.0024033934
## nodefactor.vertex.names.VP 0.0121011485
## nodefactor.vertex.names.Ri
## edges 0.1185407350
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## nodefactor.vertex.names.SII 0.0259367455
## nodefactor.vertex.names.SMA 0.0269620083
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## nodefactor.vertex.names.VP 0.0087368108
## nodefactor.vertex.names.SII
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## nodefactor.vertex.names.SII 1.0000000000
## nodefactor.vertex.names.SMA -0.0121439003
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## nodefactor.vertex.names.VP 0.0052431620
## nodefactor.vertex.names.SMA
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## nodefactor.vertex.names.SII -0.012143900
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## nodefactor.vertex.names.STPa
## edges 1.229990e-01
## mutual 1.088797e-01
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## nodefactor.vertex.names.SII 5.682609e-02
## nodefactor.vertex.names.SMA -7.551076e-03
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## nodefactor.vertex.names.Ri 0.002664973
## nodefactor.vertex.names.SII 0.060509046
## nodefactor.vertex.names.SMA 0.003886511
## nodefactor.vertex.names.STPa -0.022415956
## nodefactor.vertex.names.STPp 0.030674157
## nodefactor.vertex.names.TF 0.028150524
## nodefactor.vertex.names.TH 1.000000000
## nodefactor.vertex.names.V1 0.040368550
## nodefactor.vertex.names.V2 0.004425430
## nodefactor.vertex.names.V3 0.049729882
## nodefactor.vertex.names.V3A 0.069878994
## nodefactor.vertex.names.V4 0.041147698
## nodefactor.vertex.names.V4t 0.001644155
## nodefactor.vertex.names.VIP 0.042101026
## nodefactor.vertex.names.VOT 0.020116848
## nodefactor.vertex.names.VP -0.011548995
## nodefactor.vertex.names.V1
## edges 0.215011835
## mutual 0.208859632
## nodefactor.vertex.names.2 0.062329266
## nodefactor.vertex.names.35 0.012867803
## nodefactor.vertex.names.36 0.012400799
## nodefactor.vertex.names.3a 0.027989009
## nodefactor.vertex.names.3b 0.021936112
## nodefactor.vertex.names.4 0.066354891
## nodefactor.vertex.names.46 0.040775119
## nodefactor.vertex.names.5 0.026060266
## nodefactor.vertex.names.6 0.021350733
## nodefactor.vertex.names.7a 0.012330203
## nodefactor.vertex.names.7b 0.038165759
## nodefactor.vertex.names.AITd 0.030175859
## nodefactor.vertex.names.AITv 0.049080193
## nodefactor.vertex.names.CITd 0.019221332
## nodefactor.vertex.names.CITv 0.003525018
## nodefactor.vertex.names.DP 0.055797950
## nodefactor.vertex.names.FEF 0.004035564
## nodefactor.vertex.names.FST 0.018919171
## nodefactor.vertex.names.Id 0.037422697
## nodefactor.vertex.names.Ig 0.009110786
## nodefactor.vertex.names.LIP 0.031682120
## nodefactor.vertex.names.MSTd/p -0.004758271
## nodefactor.vertex.names.MSTl 0.028602348
## nodefactor.vertex.names.MT 0.033916648
## nodefactor.vertex.names.PIP 0.003320327
## nodefactor.vertex.names.PITd -0.009218566
## nodefactor.vertex.names.PITv -0.010930584
## nodefactor.vertex.names.PO 0.035403238
## nodefactor.vertex.names.Ri -0.004998428
## nodefactor.vertex.names.SII 0.047938186
## nodefactor.vertex.names.SMA 0.031748725
## nodefactor.vertex.names.STPa 0.022070846
## nodefactor.vertex.names.STPp 0.017027141
## nodefactor.vertex.names.TF 0.028743694
## nodefactor.vertex.names.TH 0.040368550
## nodefactor.vertex.names.V1 1.000000000
## nodefactor.vertex.names.V2 0.001134666
## nodefactor.vertex.names.V3 0.018474904
## nodefactor.vertex.names.V3A 0.069158758
## nodefactor.vertex.names.V4 0.039397152
## nodefactor.vertex.names.V4t 0.043286437
## nodefactor.vertex.names.VIP 0.039800870
## nodefactor.vertex.names.VOT 0.019528098
## nodefactor.vertex.names.VP 0.021854244
## nodefactor.vertex.names.V2
## edges 0.2433134763
## mutual 0.2316545815
## nodefactor.vertex.names.2 0.0147050817
## nodefactor.vertex.names.35 0.0221699803
## nodefactor.vertex.names.36 0.0010939084
## nodefactor.vertex.names.3a 0.0630245992
## nodefactor.vertex.names.3b 0.0048651769
## nodefactor.vertex.names.4 0.0288416718
## nodefactor.vertex.names.46 0.0188203081
## nodefactor.vertex.names.5 0.0379784709
## nodefactor.vertex.names.6 0.0136530163
## nodefactor.vertex.names.7a 0.0492156596
## nodefactor.vertex.names.7b 0.0403369163
## nodefactor.vertex.names.AITd 0.0095688188
## nodefactor.vertex.names.AITv -0.0063768252
## nodefactor.vertex.names.CITd 0.0327541859
## nodefactor.vertex.names.CITv 0.0332773187
## nodefactor.vertex.names.DP 0.0033452710
## nodefactor.vertex.names.FEF 0.0427709743
## nodefactor.vertex.names.FST 0.0449451742
## nodefactor.vertex.names.Id 0.0141527713
## nodefactor.vertex.names.Ig 0.0372591680
## nodefactor.vertex.names.LIP 0.0409091657
## nodefactor.vertex.names.MSTd/p 0.0239586046
## nodefactor.vertex.names.MSTl 0.0722031895
## nodefactor.vertex.names.MT 0.0233877015
## nodefactor.vertex.names.PIP 0.0484880504
## nodefactor.vertex.names.PITd 0.0515432044
## nodefactor.vertex.names.PITv 0.0261993716
## nodefactor.vertex.names.PO 0.0446521205
## nodefactor.vertex.names.Ri -0.0061235908
## nodefactor.vertex.names.SII 0.0116899311
## nodefactor.vertex.names.SMA 0.0363811968
## nodefactor.vertex.names.STPa -0.0009475339
## nodefactor.vertex.names.STPp 0.0443498187
## nodefactor.vertex.names.TF 0.0192979470
## nodefactor.vertex.names.TH 0.0044254299
## nodefactor.vertex.names.V1 0.0011346660
## nodefactor.vertex.names.V2 1.0000000000
## nodefactor.vertex.names.V3 0.0607727554
## nodefactor.vertex.names.V3A 0.0260871383
## nodefactor.vertex.names.V4 0.0248621484
## nodefactor.vertex.names.V4t -0.0091948441
## nodefactor.vertex.names.VIP 0.0523407527
## nodefactor.vertex.names.VOT 0.0049412090
## nodefactor.vertex.names.VP 0.0416220665
## nodefactor.vertex.names.V3
## edges 0.2298051152
## mutual 0.2232689170
## nodefactor.vertex.names.2 0.0200689532
## nodefactor.vertex.names.35 0.0357353728
## nodefactor.vertex.names.36 0.0418512581
## nodefactor.vertex.names.3a 0.0133606120
## nodefactor.vertex.names.3b 0.0265226346
## nodefactor.vertex.names.4 0.0271845911
## nodefactor.vertex.names.46 0.0369847932
## nodefactor.vertex.names.5 0.0318286385
## nodefactor.vertex.names.6 0.0727029328
## nodefactor.vertex.names.7a 0.0195551776
## nodefactor.vertex.names.7b 0.0295796372
## nodefactor.vertex.names.AITd 0.0179376742
## nodefactor.vertex.names.AITv 0.0151508138
## nodefactor.vertex.names.CITd 0.0145913443
## nodefactor.vertex.names.CITv 0.0364090722
## nodefactor.vertex.names.DP -0.0137403259
## nodefactor.vertex.names.FEF 0.0590395800
## nodefactor.vertex.names.FST 0.0105982964
## nodefactor.vertex.names.Id 0.0118226586
## nodefactor.vertex.names.Ig 0.0004386472
## nodefactor.vertex.names.LIP 0.0030923672
## nodefactor.vertex.names.MSTd/p 0.0281146808
## nodefactor.vertex.names.MSTl 0.0159978640
## nodefactor.vertex.names.MT 0.0415203427
## nodefactor.vertex.names.PIP 0.0298114956
## nodefactor.vertex.names.PITd 0.0413590831
## nodefactor.vertex.names.PITv 0.0225192887
## nodefactor.vertex.names.PO -0.0300127372
## nodefactor.vertex.names.Ri -0.0058850215
## nodefactor.vertex.names.SII 0.0052929218
## nodefactor.vertex.names.SMA 0.0489879708
## nodefactor.vertex.names.STPa 0.0296675240
## nodefactor.vertex.names.STPp 0.0093347075
## nodefactor.vertex.names.TF 0.0441026565
## nodefactor.vertex.names.TH 0.0497298815
## nodefactor.vertex.names.V1 0.0184749042
## nodefactor.vertex.names.V2 0.0607727554
## nodefactor.vertex.names.V3 1.0000000000
## nodefactor.vertex.names.V3A 0.0209978434
## nodefactor.vertex.names.V4 0.0258862844
## nodefactor.vertex.names.V4t 0.0290762060
## nodefactor.vertex.names.VIP -0.0030246438
## nodefactor.vertex.names.VOT 0.0258986774
## nodefactor.vertex.names.VP 0.0202097747
## nodefactor.vertex.names.V3A
## edges 0.2522668171
## mutual 0.2399200356
## nodefactor.vertex.names.2 0.0553502165
## nodefactor.vertex.names.35 0.0088886920
## nodefactor.vertex.names.36 0.0364599051
## nodefactor.vertex.names.3a 0.0196776428
## nodefactor.vertex.names.3b 0.0003022737
## nodefactor.vertex.names.4 0.0311883265
## nodefactor.vertex.names.46 0.0697663868
## nodefactor.vertex.names.5 0.0482070380
## nodefactor.vertex.names.6 0.0184166331
## nodefactor.vertex.names.7a 0.0501460271
## nodefactor.vertex.names.7b 0.0244309086
## nodefactor.vertex.names.AITd 0.0213644015
## nodefactor.vertex.names.AITv 0.0370175980
## nodefactor.vertex.names.CITd 0.0142116324
## nodefactor.vertex.names.CITv 0.0420065937
## nodefactor.vertex.names.DP 0.0564086572
## nodefactor.vertex.names.FEF 0.0131394089
## nodefactor.vertex.names.FST 0.0058788816
## nodefactor.vertex.names.Id 0.0321443989
## nodefactor.vertex.names.Ig 0.0334752356
## nodefactor.vertex.names.LIP 0.0311623576
## nodefactor.vertex.names.MSTd/p 0.0011965740
## nodefactor.vertex.names.MSTl 0.0475815032
## nodefactor.vertex.names.MT 0.0845773395
## nodefactor.vertex.names.PIP 0.0213460572
## nodefactor.vertex.names.PITd 0.0249814255
## nodefactor.vertex.names.PITv 0.0207067472
## nodefactor.vertex.names.PO -0.0087508130
## nodefactor.vertex.names.Ri 0.0403496923
## nodefactor.vertex.names.SII 0.0403244579
## nodefactor.vertex.names.SMA -0.0052608999
## nodefactor.vertex.names.STPa 0.0055856518
## nodefactor.vertex.names.STPp -0.0266441092
## nodefactor.vertex.names.TF 0.0556048622
## nodefactor.vertex.names.TH 0.0698789945
## nodefactor.vertex.names.V1 0.0691587579
## nodefactor.vertex.names.V2 0.0260871383
## nodefactor.vertex.names.V3 0.0209978434
## nodefactor.vertex.names.V3A 1.0000000000
## nodefactor.vertex.names.V4 0.0388423500
## nodefactor.vertex.names.V4t 0.0133021038
## nodefactor.vertex.names.VIP 0.0477421133
## nodefactor.vertex.names.VOT 0.0233606362
## nodefactor.vertex.names.VP 0.0188486690
## nodefactor.vertex.names.V4
## edges 0.2579612914
## mutual 0.2545627786
## nodefactor.vertex.names.2 0.0546556918
## nodefactor.vertex.names.35 0.0378334840
## nodefactor.vertex.names.36 0.0173357182
## nodefactor.vertex.names.3a -0.0048146753
## nodefactor.vertex.names.3b 0.0331966019
## nodefactor.vertex.names.4 0.0166575014
## nodefactor.vertex.names.46 0.0371621294
## nodefactor.vertex.names.5 0.0695690510
## nodefactor.vertex.names.6 0.0408962086
## nodefactor.vertex.names.7a 0.0235020931
## nodefactor.vertex.names.7b 0.0200800401
## nodefactor.vertex.names.AITd 0.0430460693
## nodefactor.vertex.names.AITv 0.0183458524
## nodefactor.vertex.names.CITd 0.0006009402
## nodefactor.vertex.names.CITv 0.0170319516
## nodefactor.vertex.names.DP 0.0251255089
## nodefactor.vertex.names.FEF 0.0141009515
## nodefactor.vertex.names.FST 0.0338311882
## nodefactor.vertex.names.Id 0.0227357521
## nodefactor.vertex.names.Ig 0.0380723914
## nodefactor.vertex.names.LIP 0.0211408466
## nodefactor.vertex.names.MSTd/p 0.0418850621
## nodefactor.vertex.names.MSTl 0.0240925792
## nodefactor.vertex.names.MT -0.0193288427
## nodefactor.vertex.names.PIP 0.0001634708
## nodefactor.vertex.names.PITd 0.0435880127
## nodefactor.vertex.names.PITv 0.0634334288
## nodefactor.vertex.names.PO 0.0057444195
## nodefactor.vertex.names.Ri 0.0381003789
## nodefactor.vertex.names.SII 0.0317317944
## nodefactor.vertex.names.SMA 0.0476377839
## nodefactor.vertex.names.STPa 0.0299208630
## nodefactor.vertex.names.STPp 0.0065950748
## nodefactor.vertex.names.TF 0.0426292536
## nodefactor.vertex.names.TH 0.0411476984
## nodefactor.vertex.names.V1 0.0393971518
## nodefactor.vertex.names.V2 0.0248621484
## nodefactor.vertex.names.V3 0.0258862844
## nodefactor.vertex.names.V3A 0.0388423500
## nodefactor.vertex.names.V4 1.0000000000
## nodefactor.vertex.names.V4t 0.0477031639
## nodefactor.vertex.names.VIP 0.0411581964
## nodefactor.vertex.names.VOT 0.0269338703
## nodefactor.vertex.names.VP 0.0442118556
## nodefactor.vertex.names.V4t
## edges 0.206789886
## mutual 0.199569204
## nodefactor.vertex.names.2 0.030542168
## nodefactor.vertex.names.35 0.026543386
## nodefactor.vertex.names.36 0.013970520
## nodefactor.vertex.names.3a 0.020912200
## nodefactor.vertex.names.3b 0.009493067
## nodefactor.vertex.names.4 -0.027938404
## nodefactor.vertex.names.46 0.049375096
## nodefactor.vertex.names.5 0.028698906
## nodefactor.vertex.names.6 0.041766633
## nodefactor.vertex.names.7a 0.028482041
## nodefactor.vertex.names.7b 0.016414048
## nodefactor.vertex.names.AITd -0.023189832
## nodefactor.vertex.names.AITv 0.048081098
## nodefactor.vertex.names.CITd 0.008060640
## nodefactor.vertex.names.CITv 0.029934375
## nodefactor.vertex.names.DP 0.046359678
## nodefactor.vertex.names.FEF 0.031597631
## nodefactor.vertex.names.FST 0.055176570
## nodefactor.vertex.names.Id 0.030963384
## nodefactor.vertex.names.Ig 0.021415134
## nodefactor.vertex.names.LIP 0.045954679
## nodefactor.vertex.names.MSTd/p 0.053778863
## nodefactor.vertex.names.MSTl 0.015232134
## nodefactor.vertex.names.MT 0.045798656
## nodefactor.vertex.names.PIP 0.021390058
## nodefactor.vertex.names.PITd -0.010829238
## nodefactor.vertex.names.PITv 0.037679228
## nodefactor.vertex.names.PO -0.003889949
## nodefactor.vertex.names.Ri 0.010121768
## nodefactor.vertex.names.SII -0.011872133
## nodefactor.vertex.names.SMA 0.012695385
## nodefactor.vertex.names.STPa 0.035279884
## nodefactor.vertex.names.STPp 0.037220485
## nodefactor.vertex.names.TF 0.037955943
## nodefactor.vertex.names.TH 0.001644155
## nodefactor.vertex.names.V1 0.043286437
## nodefactor.vertex.names.V2 -0.009194844
## nodefactor.vertex.names.V3 0.029076206
## nodefactor.vertex.names.V3A 0.013302104
## nodefactor.vertex.names.V4 0.047703164
## nodefactor.vertex.names.V4t 1.000000000
## nodefactor.vertex.names.VIP 0.015151452
## nodefactor.vertex.names.VOT 0.047170717
## nodefactor.vertex.names.VP 0.026710510
## nodefactor.vertex.names.VIP
## edges 0.2551025105
## mutual 0.2542918092
## nodefactor.vertex.names.2 0.0774681897
## nodefactor.vertex.names.35 0.0384029901
## nodefactor.vertex.names.36 0.0036110490
## nodefactor.vertex.names.3a 0.0450566214
## nodefactor.vertex.names.3b 0.0296596531
## nodefactor.vertex.names.4 0.0552474808
## nodefactor.vertex.names.46 0.0155985298
## nodefactor.vertex.names.5 0.0394142505
## nodefactor.vertex.names.6 0.0164898168
## nodefactor.vertex.names.7a 0.0182751708
## nodefactor.vertex.names.7b 0.0346250866
## nodefactor.vertex.names.AITd 0.0328334101
## nodefactor.vertex.names.AITv 0.0595726505
## nodefactor.vertex.names.CITd 0.0243731565
## nodefactor.vertex.names.CITv 0.0298816882
## nodefactor.vertex.names.DP 0.0521296661
## nodefactor.vertex.names.FEF 0.0296309881
## nodefactor.vertex.names.FST 0.0185600044
## nodefactor.vertex.names.Id 0.0070032109
## nodefactor.vertex.names.Ig 0.0404425813
## nodefactor.vertex.names.LIP 0.0157659147
## nodefactor.vertex.names.MSTd/p 0.0096579734
## nodefactor.vertex.names.MSTl 0.0563880104
## nodefactor.vertex.names.MT 0.0425508628
## nodefactor.vertex.names.PIP 0.0325647207
## nodefactor.vertex.names.PITd 0.0266087268
## nodefactor.vertex.names.PITv 0.0180632627
## nodefactor.vertex.names.PO 0.0286522218
## nodefactor.vertex.names.Ri -0.0144995775
## nodefactor.vertex.names.SII 0.0446317354
## nodefactor.vertex.names.SMA 0.0247642598
## nodefactor.vertex.names.STPa 0.0252774347
## nodefactor.vertex.names.STPp -0.0013431072
## nodefactor.vertex.names.TF -0.0008349996
## nodefactor.vertex.names.TH 0.0421010261
## nodefactor.vertex.names.V1 0.0398008696
## nodefactor.vertex.names.V2 0.0523407527
## nodefactor.vertex.names.V3 -0.0030246438
## nodefactor.vertex.names.V3A 0.0477421133
## nodefactor.vertex.names.V4 0.0411581964
## nodefactor.vertex.names.V4t 0.0151514519
## nodefactor.vertex.names.VIP 1.0000000000
## nodefactor.vertex.names.VOT 0.0394924127
## nodefactor.vertex.names.VP 0.0426165647
## nodefactor.vertex.names.VOT
## edges 0.1417486071
## mutual 0.1296544027
## nodefactor.vertex.names.2 0.0522702595
## nodefactor.vertex.names.35 0.0295118342
## nodefactor.vertex.names.36 -0.0200142014
## nodefactor.vertex.names.3a 0.0323040153
## nodefactor.vertex.names.3b 0.0178699680
## nodefactor.vertex.names.4 0.0442256196
## nodefactor.vertex.names.46 0.0815373864
## nodefactor.vertex.names.5 0.0275140019
## nodefactor.vertex.names.6 -0.0294649787
## nodefactor.vertex.names.7a -0.0033666908
## nodefactor.vertex.names.7b -0.0046597487
## nodefactor.vertex.names.AITd 0.0402820301
## nodefactor.vertex.names.AITv 0.0058508025
## nodefactor.vertex.names.CITd 0.0040553070
## nodefactor.vertex.names.CITv -0.0420320042
## nodefactor.vertex.names.DP 0.0299721646
## nodefactor.vertex.names.FEF 0.0462374624
## nodefactor.vertex.names.FST 0.0210972621
## nodefactor.vertex.names.Id -0.0107834226
## nodefactor.vertex.names.Ig -0.0343215406
## nodefactor.vertex.names.LIP 0.0003774673
## nodefactor.vertex.names.MSTd/p -0.0014014848
## nodefactor.vertex.names.MSTl -0.0008206584
## nodefactor.vertex.names.MT 0.0170406528
## nodefactor.vertex.names.PIP -0.0330698379
## nodefactor.vertex.names.PITd 0.0188739969
## nodefactor.vertex.names.PITv -0.0230773595
## nodefactor.vertex.names.PO -0.0024033934
## nodefactor.vertex.names.Ri 0.0311076916
## nodefactor.vertex.names.SII 0.0046071287
## nodefactor.vertex.names.SMA 0.0297631408
## nodefactor.vertex.names.STPa 0.0182688568
## nodefactor.vertex.names.STPp -0.0046417997
## nodefactor.vertex.names.TF 0.0322649568
## nodefactor.vertex.names.TH 0.0201168476
## nodefactor.vertex.names.V1 0.0195280982
## nodefactor.vertex.names.V2 0.0049412090
## nodefactor.vertex.names.V3 0.0258986774
## nodefactor.vertex.names.V3A 0.0233606362
## nodefactor.vertex.names.V4 0.0269338703
## nodefactor.vertex.names.V4t 0.0471707174
## nodefactor.vertex.names.VIP 0.0394924127
## nodefactor.vertex.names.VOT 1.0000000000
## nodefactor.vertex.names.VP 0.0226657030
## nodefactor.vertex.names.VP
## edges 0.2294039337
## mutual 0.2201490967
## nodefactor.vertex.names.2 0.0307352907
## nodefactor.vertex.names.35 0.0468788428
## nodefactor.vertex.names.36 0.0321621013
## nodefactor.vertex.names.3a 0.0348318704
## nodefactor.vertex.names.3b 0.0441332765
## nodefactor.vertex.names.4 0.0005472012
## nodefactor.vertex.names.46 0.0269376580
## nodefactor.vertex.names.5 0.0049769691
## nodefactor.vertex.names.6 0.0566882287
## nodefactor.vertex.names.7a 0.0732281894
## nodefactor.vertex.names.7b -0.0011018325
## nodefactor.vertex.names.AITd 0.0445562096
## nodefactor.vertex.names.AITv 0.0205507387
## nodefactor.vertex.names.CITd 0.0405566960
## nodefactor.vertex.names.CITv 0.0530510254
## nodefactor.vertex.names.DP 0.0033396272
## nodefactor.vertex.names.FEF 0.0353784006
## nodefactor.vertex.names.FST 0.0165208958
## nodefactor.vertex.names.Id 0.0175348859
## nodefactor.vertex.names.Ig 0.0433941206
## nodefactor.vertex.names.LIP -0.0003976271
## nodefactor.vertex.names.MSTd/p 0.0018983299
## nodefactor.vertex.names.MSTl 0.0703668959
## nodefactor.vertex.names.MT 0.0442327205
## nodefactor.vertex.names.PIP 0.0262845340
## nodefactor.vertex.names.PITd 0.0284597833
## nodefactor.vertex.names.PITv 0.0061114544
## nodefactor.vertex.names.PO 0.0121011485
## nodefactor.vertex.names.Ri 0.0087368108
## nodefactor.vertex.names.SII 0.0052431620
## nodefactor.vertex.names.SMA 0.0162785129
## nodefactor.vertex.names.STPa 0.0129071567
## nodefactor.vertex.names.STPp 0.0195655371
## nodefactor.vertex.names.TF 0.0143624864
## nodefactor.vertex.names.TH -0.0115489948
## nodefactor.vertex.names.V1 0.0218542438
## nodefactor.vertex.names.V2 0.0416220665
## nodefactor.vertex.names.V3 0.0202097747
## nodefactor.vertex.names.V3A 0.0188486690
## nodefactor.vertex.names.V4 0.0442118556
## nodefactor.vertex.names.V4t 0.0267105105
## nodefactor.vertex.names.VIP 0.0426165647
## nodefactor.vertex.names.VOT 0.0226657030
## nodefactor.vertex.names.VP 1.0000000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges mutual nodefactor.vertex.names.2
## Lag 0 1.00000000 1.00000000 1.00000000
## Lag 8192 0.54904990 0.56916401 0.53952562
## Lag 16384 0.31342617 0.32860514 0.29746521
## Lag 24576 0.17726232 0.19174435 0.14681865
## Lag 32768 0.11081190 0.13001530 0.08493264
## Lag 40960 0.07011538 0.08508379 0.05412579
## nodefactor.vertex.names.35 nodefactor.vertex.names.36
## Lag 0 1.000000000 1.00000000
## Lag 8192 0.337019558 0.41204483
## Lag 16384 0.118707443 0.19731975
## Lag 24576 0.003421144 0.09273336
## Lag 32768 -0.026608643 0.05438648
## Lag 40960 -0.020919260 0.02613485
## nodefactor.vertex.names.3a nodefactor.vertex.names.3b
## Lag 0 1.00000000 1.00000000
## Lag 8192 0.46872398 0.41416749
## Lag 16384 0.22472437 0.20191141
## Lag 24576 0.08903029 0.12771695
## Lag 32768 0.02011055 0.06176647
## Lag 40960 -0.01329854 0.02255422
## nodefactor.vertex.names.4 nodefactor.vertex.names.46
## Lag 0 1.00000000 1.00000000
## Lag 8192 0.52238975 0.60128661
## Lag 16384 0.27725158 0.36930356
## Lag 24576 0.13724461 0.22154841
## Lag 32768 0.06639837 0.11894798
## Lag 40960 0.01307976 0.05472708
## nodefactor.vertex.names.5 nodefactor.vertex.names.6
## Lag 0 1.00000000 1.00000000
## Lag 8192 0.51722401 0.52573196
## Lag 16384 0.30618517 0.30213014
## Lag 24576 0.18715995 0.17128953
## Lag 32768 0.08835623 0.09647327
## Lag 40960 0.06328695 0.03047270
## nodefactor.vertex.names.7a nodefactor.vertex.names.7b
## Lag 0 1.00000000 1.00000000
## Lag 8192 0.55016509 0.54563523
## Lag 16384 0.32046963 0.30838021
## Lag 24576 0.17385361 0.17042707
## Lag 32768 0.09691113 0.08655622
## Lag 40960 0.03818497 0.04963866
## nodefactor.vertex.names.AITd nodefactor.vertex.names.AITv
## Lag 0 1.00000000 1.000000000
## Lag 8192 0.50085781 0.479912210
## Lag 16384 0.28075929 0.261876670
## Lag 24576 0.15621951 0.145537043
## Lag 32768 0.10341668 0.054150227
## Lag 40960 0.06998926 0.004676129
## nodefactor.vertex.names.CITd nodefactor.vertex.names.CITv
## Lag 0 1.00000000 1.00000000
## Lag 8192 0.44308214 0.51898431
## Lag 16384 0.19480239 0.29902450
## Lag 24576 0.06974918 0.18785794
## Lag 32768 0.03866006 0.09494799
## Lag 40960 0.01807733 0.05470146
## nodefactor.vertex.names.DP nodefactor.vertex.names.FEF
## Lag 0 1.00000000 1.00000000
## Lag 8192 0.51843720 0.57217516
## Lag 16384 0.26542655 0.34988614
## Lag 24576 0.11067109 0.18653501
## Lag 32768 0.03221475 0.10739123
## Lag 40960 0.01195596 0.05854526
## nodefactor.vertex.names.FST nodefactor.vertex.names.Id
## Lag 0 1.00000000 1.00000000
## Lag 8192 0.56925463 0.41107794
## Lag 16384 0.34488352 0.16985530
## Lag 24576 0.22253197 0.06775177
## Lag 32768 0.14539009 0.03297290
## Lag 40960 0.09546622 0.01333126
## nodefactor.vertex.names.Ig nodefactor.vertex.names.LIP
## Lag 0 1.00000000 1.0000000
## Lag 8192 0.46628504 0.5811724
## Lag 16384 0.23537454 0.3440359
## Lag 24576 0.11726189 0.1991053
## Lag 32768 0.04646255 0.1071245
## Lag 40960 0.02317702 0.0440612
## nodefactor.vertex.names.MSTd/p nodefactor.vertex.names.MSTl
## Lag 0 1.00000000 1.00000000
## Lag 8192 0.57259957 0.51115335
## Lag 16384 0.35232318 0.26826798
## Lag 24576 0.21364216 0.15637942
## Lag 32768 0.12181697 0.10109046
## Lag 40960 0.07838702 0.08222535
## nodefactor.vertex.names.MT nodefactor.vertex.names.PIP
## Lag 0 1.0000000 1.00000000
## Lag 8192 0.5728836 0.52194799
## Lag 16384 0.3463341 0.29624118
## Lag 24576 0.1990424 0.15017664
## Lag 32768 0.1129935 0.08897634
## Lag 40960 0.0614181 0.05286671
## nodefactor.vertex.names.PITd nodefactor.vertex.names.PITv
## Lag 0 1.00000000 1.00000000
## Lag 8192 0.48113984 0.53184365
## Lag 16384 0.26254643 0.30286383
## Lag 24576 0.13257739 0.18442403
## Lag 32768 0.07130586 0.11614823
## Lag 40960 0.05778068 0.07374681
## nodefactor.vertex.names.PO nodefactor.vertex.names.Ri
## Lag 0 1.00000000 1.00000000
## Lag 8192 0.59732806 0.40849716
## Lag 16384 0.37529354 0.18477337
## Lag 24576 0.23145972 0.09868041
## Lag 32768 0.13797082 0.04209239
## Lag 40960 0.09105611 0.02351556
## nodefactor.vertex.names.SII nodefactor.vertex.names.SMA
## Lag 0 1.00000000 1.000000000
## Lag 8192 0.54727189 0.503607875
## Lag 16384 0.31872145 0.254287288
## Lag 24576 0.19353772 0.112774244
## Lag 32768 0.09853119 0.053320328
## Lag 40960 0.05648138 0.001153251
## nodefactor.vertex.names.STPa nodefactor.vertex.names.STPp
## Lag 0 1.00000000 1.00000000
## Lag 8192 0.42485501 0.51125885
## Lag 16384 0.19000045 0.26115168
## Lag 24576 0.08503881 0.13853222
## Lag 32768 0.05055281 0.06834459
## Lag 40960 0.02373232 0.02930105
## nodefactor.vertex.names.TF nodefactor.vertex.names.TH
## Lag 0 1.0000000 1.00000000
## Lag 8192 0.5741367 0.56173762
## Lag 16384 0.3470817 0.32951747
## Lag 24576 0.2216044 0.18279699
## Lag 32768 0.1424420 0.09640744
## Lag 40960 0.1193853 0.05228853
## nodefactor.vertex.names.V1 nodefactor.vertex.names.V2
## Lag 0 1.00000000 1.0000000
## Lag 8192 0.50143083 0.5796809
## Lag 16384 0.25989713 0.3554609
## Lag 24576 0.11777765 0.2271763
## Lag 32768 0.06511476 0.1507214
## Lag 40960 0.01471137 0.1119186
## nodefactor.vertex.names.V3 nodefactor.vertex.names.V3A
## Lag 0 1.00000000 1.00000000
## Lag 8192 0.58306769 0.54097914
## Lag 16384 0.33079900 0.28644052
## Lag 24576 0.19975849 0.15253312
## Lag 32768 0.12491890 0.10619576
## Lag 40960 0.06989325 0.06169697
## nodefactor.vertex.names.V4 nodefactor.vertex.names.V4t
## Lag 0 1.00000000 1.000000000
## Lag 8192 0.58308417 0.500663149
## Lag 16384 0.34486324 0.251150562
## Lag 24576 0.21023127 0.094149459
## Lag 32768 0.12184834 0.034520821
## Lag 40960 0.06534783 0.006753972
## nodefactor.vertex.names.VIP nodefactor.vertex.names.VOT
## Lag 0 1.00000000 1.000000000
## Lag 8192 0.59475486 0.400019246
## Lag 16384 0.37276290 0.183908982
## Lag 24576 0.23978730 0.101182418
## Lag 32768 0.15136445 0.050251828
## Lag 40960 0.09061252 -0.002896266
## nodefactor.vertex.names.VP
## Lag 0 1.00000000
## Lag 8192 0.56138629
## Lag 16384 0.30586478
## Lag 24576 0.16048672
## Lag 32768 0.08946622
## Lag 40960 0.03893471
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 1
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges mutual
## -4.32731725 -3.63809713
## nodefactor.vertex.names.2 nodefactor.vertex.names.35
## -1.08919532 -0.13687060
## nodefactor.vertex.names.36 nodefactor.vertex.names.3a
## -1.08267491 0.06743824
## nodefactor.vertex.names.3b nodefactor.vertex.names.4
## -0.92107234 -3.18908903
## nodefactor.vertex.names.46 nodefactor.vertex.names.5
## -1.50079846 -0.86323072
## nodefactor.vertex.names.6 nodefactor.vertex.names.7a
## -0.31586271 -0.10537935
## nodefactor.vertex.names.7b nodefactor.vertex.names.AITd
## -2.09745105 -0.76524471
## nodefactor.vertex.names.AITv nodefactor.vertex.names.CITd
## -2.26997524 0.25673305
## nodefactor.vertex.names.CITv nodefactor.vertex.names.DP
## -0.81343370 -0.02315303
## nodefactor.vertex.names.FEF nodefactor.vertex.names.FST
## -1.28668585 0.22204174
## nodefactor.vertex.names.Id nodefactor.vertex.names.Ig
## 0.66745456 -0.76782071
## nodefactor.vertex.names.LIP nodefactor.vertex.names.MSTd/p
## -0.46660407 -0.50124058
## nodefactor.vertex.names.MSTl nodefactor.vertex.names.MT
## -0.99396402 0.72702596
## nodefactor.vertex.names.PIP nodefactor.vertex.names.PITd
## 0.52510831 0.09517957
## nodefactor.vertex.names.PITv nodefactor.vertex.names.PO
## 0.28018485 0.65768783
## nodefactor.vertex.names.Ri nodefactor.vertex.names.SII
## -0.72520114 -1.70640711
## nodefactor.vertex.names.SMA nodefactor.vertex.names.STPa
## 1.39332880 -0.35115207
## nodefactor.vertex.names.STPp nodefactor.vertex.names.TF
## -0.66893586 -2.21189028
## nodefactor.vertex.names.TH nodefactor.vertex.names.V1
## 0.11389124 -1.19631498
## nodefactor.vertex.names.V2 nodefactor.vertex.names.V3
## -0.46937749 -0.85646630
## nodefactor.vertex.names.V3A nodefactor.vertex.names.V4
## -1.18754697 -1.17313818
## nodefactor.vertex.names.V4t nodefactor.vertex.names.VIP
## 0.15686276 -2.77938040
## nodefactor.vertex.names.VOT nodefactor.vertex.names.VP
## -1.83365310 -0.77698666
##
## Individual P-values (lower = worse):
## edges mutual
## 1.509365e-05 2.746598e-04
## nodefactor.vertex.names.2 nodefactor.vertex.names.35
## 2.760678e-01 8.911331e-01
## nodefactor.vertex.names.36 nodefactor.vertex.names.3a
## 2.789527e-01 9.462328e-01
## nodefactor.vertex.names.3b nodefactor.vertex.names.4
## 3.570127e-01 1.427219e-03
## nodefactor.vertex.names.46 nodefactor.vertex.names.5
## 1.334077e-01 3.880106e-01
## nodefactor.vertex.names.6 nodefactor.vertex.names.7a
## 7.521067e-01 9.160748e-01
## nodefactor.vertex.names.7b nodefactor.vertex.names.AITd
## 3.595367e-02 4.441258e-01
## nodefactor.vertex.names.AITv nodefactor.vertex.names.CITd
## 2.320909e-02 7.973849e-01
## nodefactor.vertex.names.CITv nodefactor.vertex.names.DP
## 4.159694e-01 9.815282e-01
## nodefactor.vertex.names.FEF nodefactor.vertex.names.FST
## 1.982038e-01 8.242814e-01
## nodefactor.vertex.names.Id nodefactor.vertex.names.Ig
## 5.044818e-01 4.425937e-01
## nodefactor.vertex.names.LIP nodefactor.vertex.names.MSTd/p
## 6.407832e-01 6.162018e-01
## nodefactor.vertex.names.MSTl nodefactor.vertex.names.MT
## 3.202404e-01 4.672101e-01
## nodefactor.vertex.names.PIP nodefactor.vertex.names.PITd
## 5.995079e-01 9.241722e-01
## nodefactor.vertex.names.PITv nodefactor.vertex.names.PO
## 7.793357e-01 5.107387e-01
## nodefactor.vertex.names.Ri nodefactor.vertex.names.SII
## 4.683286e-01 8.793231e-02
## nodefactor.vertex.names.SMA nodefactor.vertex.names.STPa
## 1.635204e-01 7.254743e-01
## nodefactor.vertex.names.STPp nodefactor.vertex.names.TF
## 5.035364e-01 2.697425e-02
## nodefactor.vertex.names.TH nodefactor.vertex.names.V1
## 9.093240e-01 2.315737e-01
## nodefactor.vertex.names.V2 nodefactor.vertex.names.V3
## 6.387998e-01 3.917399e-01
## nodefactor.vertex.names.V3A nodefactor.vertex.names.V4
## 2.350119e-01 2.407404e-01
## nodefactor.vertex.names.V4t nodefactor.vertex.names.VIP
## 8.753530e-01 5.446271e-03
## nodefactor.vertex.names.VOT nodefactor.vertex.names.VP
## 6.670550e-02 4.371666e-01
## Joint P-value (lower = worse): 0.3973637
##
## Note: To save space, only one in every 2 iterations of the MCMC sample
## used for estimation was stored for diagnostics. Sample size per chain
## was originally around 7860 with thinning interval 4096.
##
## Note: MCMC diagnostics shown here are from the last round of
## simulation, prior to computation of final parameter estimates.
## Because the final estimates are refinements of those used for this
## simulation run, these diagnostics may understate model performance.
## To directly assess the performance of the final model on in-model
## statistics, please use the GOF command: gof(ergmFitObject,
## GOF=~model).Based on the MCMC chains plot, the model did pretty good in all of the variables. According to the results of the MCMC chains plot, all of the variables have created a credible posterior distribution, as it is equally distributed around zero and has a bell-shaped histogram.
sim <- simulate(model2)
tt <- graph_from_edgelist(as.matrix(as.edgelist(net_sna), directed = T))
tt1 <- graph_from_edgelist(as.matrix(as.edgelist(sim), directed = T))
igraph::reciprocity(tt)## [1] 0.8984881igraph::reciprocity(tt1)## [1] 0.886918igraph::transitivity(tt)## [1] 0.5187266igraph::transitivity(tt1)## [1] 0.3361911igraph::edge_density(tt)## [1] 0.2338384igraph::edge_density(tt1)## [1] 0.2277778set.seed(1)
net2 <- ggraph(tt) +
geom_edge_link0(color = "black", alpha = .5) +
geom_node_point(fill = "#EFD9D3",
color = "black", shape = 21) +
ggnetwork::theme_blank() +
labs(title = "Illustrative") +
theme(legend.position = "none", plot.title = element_text(hjust = 0.5))## Using "stress" as default layoutergm1_net <- ggraph(tt1) +
geom_edge_link0(color = "black", alpha = .5) +
geom_node_point(fill = "#B0A498", color = "black", shape = 21) +
labs(title = "Simulation 1") +
ggnetwork::theme_blank() +
theme(legend.position = "none", plot.title = element_text(hjust = 0.5))## Using "stress" as default layoutnets <- ggarrange(net2, ergm1_net)
nets
Community Detection
library(igraph)
library(ggraph)
net_undirected <- as.undirected(net, mode = "collapse")
## Fluid clusters####
set.seed(1)
cfc <- cluster_fluid_communities(net_undirected, no.of.communities = 4)
modularity(cfc) ## [1] 0.3582161membership(cfc) ## V1 V2 V3 V3A V4 V4t VOT VP MT MSTd/p MSTl
## 3 3 3 3 2 3 2 3 3 3 3
## PO LIP PIP VIP DP 7a FST PITd PITv CITd CITv
## 3 3 3 1 3 3 3 2 2 2 2
## AITd AITv STPp STPa TF TH FEF 46 3a 3b 1
## 2 2 4 4 4 2 3 4 1 1 1
## 2 5 Ri SII 7b 4 6 SMA Ig Id 35
## 1 1 1 1 1 1 1 1 4 4 4
## 36
## 4length(cfc)## [1] 4sizes(cfc)## Community sizes
## 1 2 3 4
## 12 9 16 8colors <- colorpanel(length(cfc), low = "#efd9d3", high = "#57424a")
cfc_plot <- ggraph(net_undirected) +
geom_edge_link0(color = "black", alpha = .5) +
geom_node_point(aes(fill = as.factor(membership(cfc))),
size = igraph::degree(net_undirected, mode = "all"),
color = "black", shape = 21) +
scale_fill_manual(values = c(colors)) +
ggnetwork::theme_blank() +
geom_node_text(aes(label = as.factor(membership(cfc))), repel = T) +
theme(legend.position = "none")## Using "stress" as default layoutcfc_plot
## Leading Eigenvector####
set.seed(1)
cle <- cluster_leading_eigen(net_undirected)
modularity(cle) ## [1] 0.3563245membership(cle) ## V1 V2 V3 V3A V4 V4t VOT VP MT MSTd/p MSTl
## 1 1 1 1 3 1 3 1 1 1 1
## PO LIP PIP VIP DP 7a FST PITd PITv CITd CITv
## 1 1 1 2 1 2 1 3 3 3 3
## AITd AITv STPp STPa TF TH FEF 46 3a 3b 1
## 3 3 3 4 3 3 1 4 2 2 2
## 2 5 Ri SII 7b 4 6 SMA Ig Id 35
## 2 2 2 2 2 2 2 2 4 4 4
## 36
## 4length(cle)## [1] 4sizes(cle)## Community sizes
## 1 2 3 4
## 15 13 11 6colors <- colorpanel(length(cle), low = "#efd9d3", high = "#57424a")
cle_plot <- ggraph(net_undirected) +
geom_edge_link0(color = "black", alpha = .5) +
geom_node_point(aes(fill = as.factor(membership(cle))),
size = igraph::degree(net_undirected, mode = "all"),
color = "black", shape = 21) +
scale_fill_manual(values = c(colors)) +
ggnetwork::theme_blank() +
geom_node_text(aes(label = as.factor(membership(cle))), repel = T) +
theme(legend.position = "none")## Using "stress" as default layoutcle_plot
## Walktrap####
set.seed(1)
cwt <- cluster_walktrap(net_undirected)
modularity(cwt) ## [1] 0.3213072membership(cwt) ## V1 V2 V3 V3A V4 V4t VOT VP MT MSTd/p MSTl
## 2 2 2 2 2 2 2 2 2 2 2
## PO LIP PIP VIP DP 7a FST PITd PITv CITd CITv
## 2 2 2 1 2 2 2 4 4 4 4
## AITd AITv STPp STPa TF TH FEF 46 3a 3b 1
## 4 4 4 4 2 4 2 2 1 1 1
## 2 5 Ri SII 7b 4 6 SMA Ig Id 35
## 1 1 1 1 1 1 1 1 3 3 3
## 36
## 3length(cwt)## [1] 4sizes(cwt)## Community sizes
## 1 2 3 4
## 12 20 4 9colors <- colorpanel(length(cwt), low = "#efd9d3", high = "#57424a")
cwt_plot <- ggraph(net_undirected) +
geom_edge_link0(color = "black", alpha = .5) +
geom_node_point(aes(fill = as.factor(membership(cwt))),
size = igraph::degree(net_undirected, mode = "all"),
color = "black", shape = 21) +
scale_fill_manual(values = c(colors)) +
ggnetwork::theme_blank() +
geom_node_text(aes(label = as.factor(membership(cwt))), repel = T) +
theme(legend.position = "none")## Using "stress" as default layoutcwt_plot
## Label Propagation Techniques####
set.seed(1)
clp <- cluster_label_prop(net_undirected)
csg <- cluster_spinglass(net_undirected)
modularity(clp) ## [1] 0.2302115membership(clp) ## V1 V2 V3 V3A V4 V4t VOT VP MT MSTd/p MSTl
## 1 1 1 1 1 1 1 1 1 1 1
## PO LIP PIP VIP DP 7a FST PITd PITv CITd CITv
## 1 1 1 1 1 1 1 1 1 1 1
## AITd AITv STPp STPa TF TH FEF 46 3a 3b 1
## 1 1 1 1 1 1 1 1 2 2 2
## 2 5 Ri SII 7b 4 6 SMA Ig Id 35
## 2 2 2 2 2 2 2 2 2 2 1
## 36
## 1length(clp)## [1] 2sizes(clp)## Community sizes
## 1 2
## 32 13colors <- colorpanel(length(clp), low = "#efd9d3", high = "#57424a")
clp_plot <- ggraph(net_undirected) +
geom_edge_link0(color = "black", alpha = .5) +
geom_node_point(aes(fill = as.factor(membership(clp))),
size = igraph::degree(net_undirected, mode = "all"),
color = "black", shape = 21) +
scale_fill_manual(values = c(colors)) +
ggnetwork::theme_blank() +
geom_node_text(aes(label = as.factor(membership(clp))), repel = T) +
theme(legend.position = "none")## Using "stress" as default layout## Optimal community structure####
set.seed(1)
clo <- cluster_optimal(net_undirected)
modularity(clo) ## [1] 0.3750404membership(clo) ## V1 V2 V3 V3A V4 V4t VOT VP MT MSTd/p MSTl
## 1 1 1 1 2 1 2 1 1 1 1
## PO LIP PIP VIP DP 7a FST PITd PITv CITd CITv
## 1 1 1 3 1 1 1 2 2 2 2
## AITd AITv STPp STPa TF TH FEF 46 3a 3b 1
## 2 2 2 2 2 2 1 2 3 3 3
## 2 5 Ri SII 7b 4 6 SMA Ig Id 35
## 3 3 3 3 3 3 3 3 3 3 3
## 36
## 2length(clo)## [1] 3sizes(clo)## Community sizes
## 1 2 3
## 16 14 15colors <- colorpanel(length(clo), low = "#efd9d3", high = "#57424a")
clo_plot <- ggraph(net_undirected) +
geom_edge_link0(color = "black", alpha = .5) +
geom_node_point(aes(fill = as.factor(membership(clo))),
size = igraph::degree(net_undirected, mode = "all"),
color = "black", shape = 21) +
scale_fill_manual(values = c(colors)) +
ggnetwork::theme_blank() +
geom_node_text(aes(label = as.factor(membership(clo))), repel = T) +
theme(legend.position = "none")## Using "stress" as default layout## Fast-Greedy Techniques####
set.seed(1)
s_un_net <- simplify(net_undirected, remove.multiple = T, remove.loops = T)
cfg <- cluster_fast_greedy(s_un_net)
modularity(cfg) ## [1] 0.3504729membership(cfg) ## V1 V2 V3 V3A V4 V4t VOT VP MT MSTd/p MSTl
## 2 2 2 2 3 2 3 2 2 2 2
## PO LIP PIP VIP DP 7a FST PITd PITv CITd CITv
## 2 2 2 1 2 1 3 3 3 3 3
## AITd AITv STPp STPa TF TH FEF 46 3a 3b 1
## 3 3 3 3 3 3 2 1 1 1 1
## 2 5 Ri SII 7b 4 6 SMA Ig Id 35
## 1 1 1 1 1 1 1 1 1 1 1
## 36
## 1length(cfg)## [1] 3sizes(cfg)## Community sizes
## 1 2 3
## 18 14 13colors <- colorpanel(length(cfg), low = "#efd9d3", high = "#57424a")
cfg_plot <- ggraph(net_undirected) +
geom_edge_link0(color = "black", alpha = .5) +
geom_node_point(aes(fill = as.factor(membership(cfg))),
size = igraph::degree(net_undirected, mode = "all"),
color = "black", shape = 21) +
scale_fill_manual(values = c(colors)) +
ggnetwork::theme_blank() +
geom_node_text(aes(label = as.factor(membership(cfg))), repel = T) +
theme(legend.position = "none")## Using "stress" as default layout## InfoMAP####
set.seed(1)
cim <- cluster_infomap(net_undirected)
modularity(cim) ## [1] 0.2755863membership(cim) ## V1 V2 V3 V3A V4 V4t VOT VP MT MSTd/p MSTl
## 1 1 1 1 1 1 1 1 1 1 1
## PO LIP PIP VIP DP 7a FST PITd PITv CITd CITv
## 1 1 1 2 1 1 1 1 1 1 1
## AITd AITv STPp STPa TF TH FEF 46 3a 3b 1
## 1 1 1 1 1 1 1 1 2 2 2
## 2 5 Ri SII 7b 4 6 SMA Ig Id 35
## 2 2 2 2 2 2 2 2 3 3 3
## 36
## 3length(cim)## [1] 3sizes(cim)## Community sizes
## 1 2 3
## 29 12 4colors <- colorpanel(length(cim), low = "#efd9d3", high = "#57424a")
cim_plot <- ggraph(net_undirected) +
geom_edge_link0(color = "black", alpha = .5) +
geom_node_point(aes(fill = as.factor(membership(cim))),
size = igraph::degree(net_undirected, mode = "all"),
color = "black", shape = 21) +
scale_fill_manual(values = c(colors)) +
ggnetwork::theme_blank() +
geom_node_text(aes(label = as.factor(membership(cim))), repel = T) +
theme(legend.position = "none")## Using "stress" as default layoutclv <- cluster_louvain(net_undirected)
modularity(clv) ## [1] 0.3741638membership(clv) ## V1 V2 V3 V3A V4 V4t VOT VP MT MSTd/p MSTl
## 1 1 1 1 2 1 2 1 1 1 1
## PO LIP PIP VIP DP 7a FST PITd PITv CITd CITv
## 1 1 1 3 1 1 1 2 2 2 2
## AITd AITv STPp STPa TF TH FEF 46 3a 3b 1
## 2 2 2 2 2 2 1 2 3 3 3
## 2 5 Ri SII 7b 4 6 SMA Ig Id 35
## 3 3 3 3 3 3 3 3 4 4 4
## 36
## 4communities(clv)## $`1`
## [1] "V1" "V2" "V3" "V3A" "V4t" "VP" "MT" "MSTd/p"
## [9] "MSTl" "PO" "LIP" "PIP" "DP" "7a" "FST" "FEF"
##
## $`2`
## [1] "V4" "VOT" "PITd" "PITv" "CITd" "CITv" "AITd" "AITv" "STPp" "STPa"
## [11] "TF" "TH" "46"
##
## $`3`
## [1] "VIP" "3a" "3b" "1" "2" "5" "Ri" "SII" "7b" "4" "6" "SMA"
##
## $`4`
## [1] "Ig" "Id" "35" "36"length(clv)## [1] 4sizes(clv)## Community sizes
## 1 2 3 4
## 16 13 12 4colors <- colorpanel(length(clv), low = "#efd9d3", high = "#57424a")
clv_plot <- ggraph(net_undirected) +
geom_edge_link0(color = "black", alpha = .5) +
geom_node_point(aes(fill = as.factor(membership(clv))),
size = igraph::degree(net_undirected, mode = "all"),
color = "black", shape = 21) +
scale_fill_manual(values = c(colors)) +
ggnetwork::theme_blank() +
geom_node_text(aes(label = as.factor(membership(clv))), repel = T) +
theme(legend.position = "none")## Using "stress" as default layoutscores <- c(fluid = modularity(cim),
eigen = modularity(cle),
walk = modularity(cwt),
spinglass = modularity(csg),
label = modularity(clp),
optimal = modularity(clo),
fastgreed = modularity(cfg),
infomap = modularity(cim),
louvain = modularity(clv))
scores## fluid eigen walk spinglass label optimal fastgreed infomap
## 0.2755863 0.3563245 0.3213072 0.3741638 0.2302115 0.3750404 0.3504729 0.2755863
## louvain
## 0.3741638#Louvain has highest modularitylcom <- cluster_louvain(net_undirected)
ocom <- optimal.community(net_undirected)
lsizes <- table(lcom$membership)
osizes <- table(ocom$membership)
lcom## IGRAPH clustering multi level, groups: 4, mod: 0.37
## + groups:
## $`1`
## [1] "V1" "V2" "V3" "V3A" "V4t" "VP" "MT" "MSTd/p"
## [9] "MSTl" "PO" "LIP" "PIP" "DP" "7a" "FST" "FEF"
##
## $`2`
## [1] "V4" "VOT" "PITd" "PITv" "CITd" "CITv" "AITd" "AITv" "STPp" "STPa"
## [11] "TF" "TH" "46"
##
## $`3`
## [1] "VIP" "3a" "3b" "1" "2" "5" "Ri" "SII" "7b" "4" "6" "SMA"
## + ... omitted several groups/verticesocom## IGRAPH clustering optimal, groups: 3, mod: 0.38
## + groups:
## $`1`
## [1] "V1" "V2" "V3" "V3A" "V4t" "VP" "MT" "MSTd/p"
## [9] "MSTl" "PO" "LIP" "PIP" "DP" "7a" "FST" "FEF"
##
## $`2`
## [1] "V4" "VOT" "PITd" "PITv" "CITd" "CITv" "AITd" "AITv" "STPp" "STPa"
## [11] "TF" "TH" "46" "36"
##
## $`3`
## [1] "VIP" "3a" "3b" "1" "2" "5" "Ri" "SII" "7b" "4" "6" "SMA"
## + ... omitted several groups/verticeslsizes##
## 1 2 3 4
## 16 13 12 4osizes##
## 1 2 3
## 16 14 15